Intelligent performance-based product recommendation system

ABSTRACT

Systems and methods of utilizing communications networks and multivariate analysis to predict or recommend optimal products from a predefined population of commercially available products are disclosed. The recommendations are based on intelligence contained in processing elements and subjective and/or objective product information received from consumers or input to the systems as part of their initial setup. The output of the systems comprise sets of products that they predict the consumer will prefer and/or perform well for the problem or concern identified by the consumer. The performance and preference predictions are a function of consumer problems and product responsiveness patterns. Objective product information is generally obtained with diagnostic instruments. Data measured with the diagnostic instruments may be communicated to the data processing portions of the invention via the Internet. The outputs of the data processing portion of the system may be presented to consumers via the Internet as well.

I. CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication Serial No. 60/241,405, filed Oct. 18, 2000, the contents ofwhich is fully incorporated herein by reference.

II. BACKGROUND

[0002] A. Field of the Invention

[0003] The present invention relates generally to systems and methodsfor generating, communicating and processing product information. Moreparticularly, the invention is directed to gathering subjective andobjective data on the effects of products from consumers and using thatdata to generate product recommendations and ancillary information, toperiodically improve the accuracy of the recommendations, and tocommunicate the product recommendations and ancillary information to theconsumers.

[0004] B. Description of the Related Art

[0005] Many commercially available products provide useful results onlyafter prolonged use. For some products, their effects may be incrementalduring the period of use. The changes wrought by the use of manyproducts therefore may not be fully appreciated by consumers. This lackof appreciation results from, among other things, the inability of theconsumer to meaningfully compare conditions pre-and post-product use.Rather, as conditions change, however incrementally, there is a tendencyto compare results to a condition after product use has begun. As aconsequence, the consumer may get a false impression of the product'sefficacy be it positive, negative, or neutral.

[0006] Further compounding the inability of consumers to meaningfullyassess the efficacy of many commercially available products is thedifficulty of testing the many options in the market and the failure orinability of many products to carry labels communicating their objectiveperformance with respect to the conditions of interest to consumers. Theadvent of the Internet and developments in the areas of recommendationsystems, neural networks, and collaborative filtering, however, nowprovide opportunities to address the foregoing problems.

[0007] The Internet and Other Public and Private Networks

[0008] The term “Internet” is an abbreviation for “Internetwork” andcommonly refers to the collection of networks and gateways that utilizethe TCP/IP suite of protocols, which are well-known in the art ofcomputer networking. TCP/IP is an acronym for “Transmission ControlProtocol/Internet Protocol.” The Internet can be described as a systemof geographically distributed remote computer networks interconnected bycomputers executing networking protocols that allow users to interactand share information over the networks. Because of such wide-spreadinformation sharing, remote networks such as the Internet have thus fargenerally evolved into an “open” system for which developers can designsoftware applications for performing specialized operations or services,essentially without restriction.

[0009] Electronic information transferred between data-processingnetworks is usually presented in hypertext, a metaphor for presentinginformation in a manner in which text, images, sounds, and actionsbecome linked together in a complex non-sequential web of associationsthat permit the user to “browse” or “navigate” through related topics,regardless of the presented order of the topics. These links are oftenestablished by both the author of a hypertext document and by the user,depending on the intent of the hypertext document. For example,traveling among links to the word “iron” in an article displayed withina graphical user interface in a data-processing system might lead theuser to the periodic table of the chemical elements (i.e., linked by theword “iron”), or to a reference to the use of iron in weapons in Europein the Dark Ages. The term “hypertext” was coined in the 1960s todescribe documents, as presented by a computer, that express thenonlinear structure of ideas, as opposed to the linear format of books,film, and speech.

[0010] A typical networked system that utilizes hypertext conventionsfollows a client/server architecture. The “client” is a member of aclass or group that uses the services of another class or group to whichit is not related. Thus, in computing, a client is a process (i.e.,roughly a set of instructions or tasks) that requests a service providedby another program. The client process utilizes the requested servicewithout having to “know” any working details about the other program orthe service itself. In a client/server architecture, particularly anetworked system, a client is usually a computer that accesses sharednetwork resources provided by another computer (i.e., a server).

[0011] Client and server communicate with one another utilizing thefunctionality provided by Hypertext-Transfer Protocol (HTTP). The WorldWide Web (WWW) or, simply, the “Web,” includes those servers adhering tothis standard (i.e., HTTP) which are accessible to clients via acomputer or data-processing system network address such as a UniformResource Locator (URL). The network address can be referred to as aUniversal Resource Locator address. For example, communication can beprovided over a communications medium. In particular, the client andserver may be coupled to one another via TCP/IP connections forhigh-capacity communication. Active within the client is a firstprocess, known as a “browser,” which establishes the connection with theserver and presents information to the user. The server itself executescorresponding server software that presents information to the client inthe form of HTTP responses. The HTTP responses correspond to “web pages”constructed from a Hypertext Markup Language (HTML), or otherserver-generated data. Each web page can also be referred to simply as a“page.”

[0012] The client typically displays the information provided throughthe network by the server, using a software application known as abrowser. Most browsers have modem graphical user interfaces that arecapable of displaying and manipulating various types of data. Agraphical user interface is a type of display format that enables a userto choose commands, start programs, and see lists of files and otheroptions by pointing to pictorial representations (icons) and lists ofmenu items on the screen. Choices can be activated generally either witha keyboard or a mouse. Internet services are typically accessed byspecifying a unique network address (i.e., typically with a URL). TheURL address has two basic components, the protocol to be used and theobject pathname. For example, the URL address, “http://www.uspto.gov”(i.e., home page for the U.S. Patent and Trademark Office), specifies aHTTP and a pathname of the server (“www.uspto.gov”). The server name isassociated with one or more equivalent TCP/IP addresses.

[0013] Neural Network Analysis

[0014] Neural network analysis is a method of modeling non-linearrelationships between independent and dependent variables. The analysisis performed by creating a network that accurately models therelationship between the independent and dependent variables. Once avalid neural network is created it can be used to predict values ofunknown, dependent variables on the basis of known, independentvariables. By convention, in neural network analysis, independentvariables are called inputs and dependent variables are called outputs.

[0015] The power of a neural network lies in the non-linear equation(s)that it uses to model the relationship(s) between the inputs and theoutputs. The equation is a complex function that is defined by a set ofvariables called connection weights. Specific values for the connectionweights are determined by a training algorithm which examines a set oftraining data. The training data is a set of inputs and associatedoutputs that are representative of the nonlinear relationship beingmodeled. The training algorithm processes the training data inputs andfinds a set of connection weights that minimize the error between thepredicted output of the neural network and the training data output.

[0016] A neural network is structurally comprised of an input layer, oneor more hidden layers, and an output layer. The output and hidden layersare comprised of interconnected processing elements, which are the mainbuilding blocks of the neural network. The primary function of the inputlayer is to route input values to processing elements of the firsthidden layer. Each processing element multiplies each input by adifferent connection weight value to obtain a product and then sums theindividual products. The results are passed through a non-lineartransfer function to produce a processing element output. All processingelement outputs of one layer are routed to processing element inputs ofthe next layer where similar processing is repeated. The final layer ina neural network is the output layer and it may contain linear and/ornon-linear processing elements. Non-linear processing elements processinputs in the same manner described above. Linear processing elementssimply pass the input of the processing element to the output of theprocessing element. The outputs of the processing elements in the outputlayer produce the final output of the neural network.

[0017] Other neural network design considerations include whether theneural network is a fully connected and/or a feedforward design. Aneural network is fully connected if all outputs from one layer are usedas inputs to the next layer. A neural network is feedforward if thereare no internal feedback loops (i.e. no outputs from one layer are usedas inputs to a previous layer).

[0018] The first step in creating a neural network is to define what isto be output. These outputs will be the final outputs of the neuralnetwork. The next step is to identify all variables that will materiallyinfluence the value of the outputs. These variables will be the inputsto the neural network. Once the network inputs and outputs have beenidentified the remaining structure of the neural network, including thenumber of layers and the number of processing elements in each layer,may be determined.

[0019] Once the structure of the neural network is determined, theneural network can be created. After creation, the neural network istrained using training data. Training data is a set of data, includinginput variables and associated output variables, which represent thestatistical relationship(s) to be modeled by the neural network. Themore training data collected and used the better, particularly if therelationship(s) being modeled is statistical in nature.

[0020] Training is accomplished by a training algorithm that isimplemented by the neural network. The training algorithm processes thetraining data and selects appropriate connection weights that mostclosely model the relationship between the training data inputs and thetraining data outputs.

[0021] Once trained, the performance of the neural network can beevaluated using test data. Testing a neural network is accomplished asfollows. Test data inputs are individually input into the neuralnetwork. The neural network is run and predicted outputs are generatedfor each test input. The predicted outputs are compared to actual testdata outputs to determine if the neural network is performing properly.A neural network that performs poorly on test data should not be used.

[0022] After a neural network is trained it can be used to predictoutputs based on various inputs. The resulting predictions then can beused for the purpose for which the neural network was designed. Examplesof neural networks are shown and described in U.S. Pat. No. 5,724,258titled “Neural Network Analysis For Multifocal Contact Lens Design,” andU.S. Pat. No. 5,839,438 titled “Computer-based Neural Network System andMethod for Medical Diagnosis and Interpretation,” both of which areincorporated herein fully by reference. Other details and principlesregarding neural networks are set forth in “Artificial Neural Networks,”Robert J. Schalkoff (McGraw-Hill, 1997), the contents of which also areincorporated herein fully by reference.

[0023] Existing Product Recommendation Systems

[0024] The rise of the Internet and its role in e-commerce has resultedin a number of product recommendation systems and methods beingdeveloped. Most of these systems share one or more of the followingobjectives and approaches. First, the systems attempt to help eachcustomer find a small, more manageable sub-set of products that may bemore valuable to him or her from amongst thousands of products. In mostcases, a customer simply could not browse the product descriptions ofthe complete set of products; and even if they could, the productdescriptions do not contain enough relevant information to enable thecustomer to assess the value of a specific product with respect to hisor her concerns and interests. Second, the systems seek to determine thecustomer's specific product preferences by analyzing the customer'spurchase behavior and product usage feedback. This kind of informationextends that available from simple, explicit customer profiles generatedthrough surveys. Third, the recommendation systems seek to exploitinformation from other customers that is similar to a given customer insome form or another.

[0025] Many of these recommendation systems utilize techniques such ascollaborative or content-based filtering to supplement informationavailable about a customer's individual behavior. The success of systemsusing techniques like filtering hinge on the assumption (reasonable inmany circumstances) that there is a material degree of overlap in theinterests, concerns, and characteristics of the numerous customersserved by the systems. However, it is often technically challenging todefine the appropriate group or “neighborhood” of similar customers fora given customer, and also to then predict the individual's preferencesfrom those in the neighborhood with present recommendation systems.Finally, some of the present recommendation systems periodically adaptthe recommendations to incorporate ongoing customer experience andbehavior, though in a very limited and simple fashion.

[0026] General categories of existing product recommendation systemsfollow. It should be noted however that many of the present systems donot fall neatly into any single category Also, the following fewcategories are not intended to be exhaustive.

[0027] One type of existing product recommendation system is anon-personalized recommendation system. Non-personalized systemsrecommend products to individual consumers based on averaged informationabout the products provided by other consumers. Examples ofnon-personalized product recommendation systems are those of Amazon.comand Moviefinder.com. The same product recommendations are made to allconsumers seeking information about a particular product(s) and allproduct recommendations are completely independent of any particularconsumer.

[0028] Another type of existing product recommendation system employsitem-to-item correlation to formulate recommendations. Item-to-itemsystems recommend other products to an individual consumer based onrelationships between products already purchased by the consumer or forwhich the consumer has expressed an interest. The relationships employedtypically are brand identity, fragrance, sales appeal, marketdistribution, and the like. In all cases the information on which therelationships are based is implicit. In other words, no explicit inputregarding what the consumer is looking for or prefers is solicited bythese systems. Rather, techniques such as data mining are employed tofind implicit relationships between products for which the individualconsumer has expressed a preference and other products available forpurchase. The actual performance of products or whether the consumer (orother consumers) ultimately did prefer the products purchased play nopart in formulating recommendations with these types of systems.

[0029] A third type of existing product recommendation system is anattribute-based system. Attribute-based recommendation systems utilizesyntactic properties or descriptive “content” of available products toformulate their recommendations. In other words, attribute-based systemsassume that the attributes of products are easily classified and that anindividual consumer knows which classification he or she should purchasewithout help or input from the recommendation system. An exemplaryattribute-based recommendation system is the MOVIE MAP service offeredby Reel.com. With the MOVIE MAP service the recommendations presentedare based solely on the category of movie selected by the consumer. Oneof the major shortcomings of attribute-based systems is that there isoften confusion among consumers and/or professionals about the properclassification of attributes to achieve successful recommendations. Forexample, in the case of automobile wax for an older vehicle there isdisagreement over whether a silicone-or wax-based cream or lotion willprovide optimal results.

[0030] In the area of product recommendation systems collaborativefiltering has proven more reliable than content-based filtering.Nonetheless, both will be discussed as certain embodiments of thepresent invention may utilize one and/or both types of filters.

[0031] Content-Based Filtering

[0032]FIG. 1 illustrates some of the principles behind content-basedfiltering. Matrix 100 is shown for a single user. The prediction isblind to data from other users, and the system assumes all productratings are binary (i.e., positive or negative). The matrix is notsparse. Assuming the category 102 is soap (S), a content-based filteringtechnique examines matrix 100 to identify the features (cost 103,fragrance 104, viscosity 105, and the like) associated with the products110 a-110 l having a rating 101 by the user (e.g., 110 b-c, 110 e, 110g, and 110 k). The appropriate features 103-105 are then used tocharacterize the user. Predicted ratings 101 for products not actuallyrated by the user (e.g., products 110 a, 110 d, 110 f, 110 h-j, and 110l) are then mapped into the feature space based on their proximity toclusters of rated products. For example, it can be deduced from theinformation about the rated products in the matrix that the consumer itcharacterizes generally prefers soaps with higher cost 103 (ranked from1 to 10, 1 representing least expensive and 10 representing mostexpensive) and have an unscented (US) fragrance 104 (scented=V and wildberry scents=WB). Therefore, although the user characterized by matrix100 has not actually rated product 110 a one might predict usingcontent-based filtering that because product 110 a is unscented andmoderately expensively the user would rate product 110 a favorably.Note, there are many techniques for determining the appropriate productfeatures to populate a feature space accurately reflecting theindividual user, and features may be added or deleted over time as thesystem learns more about a particular user or the user's preferenceschange.

[0033] Collaborative Filtering

[0034] Collaborative filtering (also referred to as social-informationfiltering) on the other hand, typically records an extended productpreference set that can be matched with a collaborative group. In otherwords, collaborative filters recommend products that “similar users”have rated highly. Often the social-information is a similar pattern ofproduct preferences.

[0035]FIG. 2 illustrates some of the principles underlying collaborativefiltering. Once again binary product ratings are assumed. Grid 200 iscomprised of product columns 201 a-201 l and user rows 202 a-202 p. Ifthe system possesses any rating data for a particular product and user,that data is entered in grid 200 at the cell formed by the intersectionof the appropriate product column and user row. For example, from grid200 it can be seen that user 202 a rated product 201 a positively. Withall the rating data entered in matrix 200 one still expects it to besparsely populated. The goal of a collaborative filter is to fill in thecells having no ratings data with accurate predictions based on theratings given by similar users mapped in matrix 200. Consider targetuser 202 i, whose ratings for products 201 a-l are enclosed in circle203. Collaborative filtering identifies user 202 o as being similar totarget user 202 i in his or her ratings of products 202 a-p (indicatedby arrow 204). Based on this similarity the system might predict thatlike user 202 o, target user 202 i would rate product 201 l positivelyas well if he or she actually used it. The system therefore mayrecommend product 201 l to target user 202 i. One skilled in the art ofcollaborative filters will appreciate that a number of techniques existfor generating predictions based on multiple similar users, forselecting appropriate subsets of nearest neighbors on which to basepredictions, for incorporating real-valued ratings in the process, andfor making real-valued predictions.

[0036] Attempts have been made to combine collaborative andcontent-based filtering in a single system. Combining collaborative andcontent-based filtering resulted in improved collaborative filteringpredictions where the user database was segmented in accordance withcontent-based filters. An exemplary combined system is described in“Recommendation as Classification: Using Social and Content-BasedInformation in Recommendation,” Proceedings of the Fifteenth NationalConference on Artificial Intelligence (AAAI-98), (Basu, C.; Hirsh, H.;and Cohen, W.; 1998), where collaborative filtering augmented bycontent-based properties is used to predict movie recommendations. Thecontent-based properties were in a database and included personnel(i.e., actors, directors, and the like), genre, language, and length ofthe movies. Improved collaborative filtering results were achieved whenthe user database was segmented on the genre of movie favored by theparticular user being served. Another example of a combinedcontent-based and collaborative filtering recommendation system isdescribed in “Combining Content-Based and Collaborative Recommendation,”Communications of the ACM, (Balabanovic, M.; Shoham, Y.; 1997)(http://citeseer.nj.nec.com/balabanovic97combining.html). TheBalabanovic and Shoham system uses content-based and collaborativefiltering to learn user interest in Internet document fetching andrecommends pre-fetch web pages for the user.

[0037] Problem Summary

[0038] Thus, it is apparent from the foregoing discussion that a numberof product recommendation systems employing numerous techniques exist inthe art. However, it is also readily apparent that presentrecommendation systems have significant shortcomings. For instance, manyif not most of the products to be considered for a particular consumermay not have been used and rated by many other consumers therebyhandicapping collaborative filtering based systems. Also, consumersoften have great difficulty in knowing or determining whether some, all,or none of their needs are being met by a particular product he or shemay be using. This is particularly true where the need being addressedby a product is characterized by an incremental response. Moreover,while existing systems may be helpful in some categories of productsthey are inappropriate where performance of the products beingrecommended is complex or even unknown. Placing a high value on theratings patterns of other consumers, even though similar in asocial-statistical sense, fails to address the likelihood that theconsumers may have disparate underlying conditions and problems to beaddressed by a product, and that the condition or problem being treatedby the product may respond quite differently. In many categories theperformance of products cannot be reliably predicted based on ratingspatterns of other similar users, promises by the manufacturers thereof,or an examination of the ingredients or makeup of the products.

[0039] Accordingly, a need exists in the art for an individualizedproduct recommendation system that does not rely primarily on consumerselection patterns but rather on product performance, optimizedsegmentation bases, and/or performance-based learning to render highlyaccurate product recommendations.

III. SUMMARY OF THE INVENTION

[0040] The present invention contemplates systems and methods ofutilizing communications networks and product recommending processingoperating on multivariate data characterizing consumers and products topredict product use effects or recommend products from a predefinedpopulation of commercially available products. The processing capabilityof the invention is based on intelligence contained in the processingcomputation design and algorithms and the data input, subjective and/orobjective product information received from consumers or input to thesystem as part of its initial setup and characterization of consumersthat allow finding commonalties among the consumers in terms of similarrequirements or responses. In one embodiment of the invention the dataprocessing portion of the system receives input from consumers via theInternet. The output(s) of the system comprise sets of products that itpredicts the consumer will prefer and/or perform well for the problem orconcern identified by the consumer. The performance and preferencepredictions are a function of consumer problems and productresponsiveness patterns. Objective product information is generallyobtained with diagnostic instruments that measure parameters havingscientific relationship to human concerns regarding a target substrateand/or that correlate with subjective performance assessments. Datameasured with the diagnostic instruments may be communicated to the dataprocessing portions of the invention via the Internet. The diagnosticinstruments may be interfaced directly to the Internet. The outputs ofthe data processing portion of the system, the product recommendationsand/or ancillary information, may be presented to consumers via theInternet as well.

[0041] The data processing portion of the invention may include a neuralnetwork. The neural network is used to model the relationship(s)(typically non-linear) between the input variables of a servedconsumer's descriptive variables and the performance and/or preferenceresponses of other consumers to products they have used in combinationwith the descriptive characterization of those consumers, and outputvariables of individual product performance and/or preferencepredictions. The neural network may be trained using actual productperformance and preference data of a subset of a relevant population. Italso may use as input product data (called product attributes) averagedover a group or segment of the consumer population along with adescriptive characterization of the group or segment to effectivelyreduce the complexity of the neural network. In certain embodiments ofthe invention the neural network is periodically re-trained through anexplicit process of evaluation and optimization utilizing comparisons ofpredicted preference and performance versus actual preference andperformance data collected from users of the invention.

[0042] Embodiments of the invention may utilize collaborative and/orcontent-based filters in the recommendation engine. Neighborhoodformation in the collaborative filters may be based on a space composedof a plurality of items, including individual concern parameters,category target conditions, patterns of performance responses toproducts, product preference patterns, product preference issues, andthe like.

[0043] Through objective and/or subjective feedback inputs, certainembodiments of the invention obtain data on the real world performanceof products, the condition of the target substrates treated, andsubstrate responses to product use. Various embodiments of the inventionuse this information, aggregated from many consumers, to recommendproducts to other consumers having a basis for similar responses. Thebasis for similarity may comprise concerns and conditions on anindividualized basis. The invention may obtain this information across apractically unlimited range of consumers and for any manufacturers'products. The invention is intended to be unbiased as to manufactureridentity or commercial intent, recommending products to a consumer basedsolely on the consumer desires in terms of performance, cost,preference, and the like.

[0044] The invention may periodically re-train its data processingportions to more accurately predict product performances and consumerpreferences. When the embodiment of the invention utilizes re-training,as the numbers of consumers and multiple feedback entries accumulate,the invention acquires greater precision based on the real worldexperiences of those consumers. This added precision often allows theinvention to differentiate between the products used in a givencategory, which may be of more value to a consumer than single productand small base size studies typical of commercial claim support andsingle product clinical trials. Where objective data is gathered, theinvention may identify correlation or other relationships with consumerconcerns to create performance response models based on the objectivedata.

[0045] Embodiments of the invention may collect data on consumerdemographics and substrate needs, including consumer preferences forproducts, the current and historical condition of the substrate to betreated (e.g., consumer's skin), and responses of the substrate tocurrent and historical product uses. For some purposes, such as creatinginformation of use in category-related industries, the invention alsomay collect data on the mean effects of products within predefined ornaturally clustering sub-populations of consumers called groups orsegments. Segments are characterized by their similarity of needs orresponses to products. In some embodiments of the invention, productrecommendation can be formed on the basis of a consumer'scharacterization similarity to segment characterizations and thesegments' average responses to products. Segments may be dynamicallydefined through re-training. Other embodiments generate data used formaking recommendations on the basis of feedback responses to productsamong dynamically constructed consumer neighborhoods defined bycollaborative filtering. Requirements. Individual consumers also mayhave access to a variety of information concerning the performance oftheir current or historical products and/or obtain system predictions ofperformance and preference of hypothetical use of available products.

IV. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] These and other features, aspects, and advantages of theinvention will become better understood in connection with the appendedclaims and the following description and drawings of various embodimentsof the invention where:

[0047]FIG. 1 shows a matrix that illustrates several principlesassociated with conventional content-based filtering techniques;

[0048]FIG. 2 shows a feature space that illustrates several principlesassociated with conventional collaborative filtering techniques;

[0049]FIG. 3 illustrates a first exemplary network environment in whichthe present invention may be employed;

[0050]FIG. 4 illustrates a second exemplary network environment in whichthe present invention may be employed;

[0051]FIG. 5 illustrates a query structure for gathering invention inputfrom a consumer in accordance with an embodiment of the invention;

[0052]FIG. 6 illustrates a consumer database entry in accordance with anembodiment of the invention;

[0053]FIG. 7 illustrates in functional form how certain embodiments ofthe invention operate when diagnostic data is incorporated therein;

[0054]FIGS. 8A and 8B illustrate exemplary top-3 recommended productlists rank ordered by predicted product preference scores;

[0055]FIGS. 9A and 9B illustrate exemplary top-3 recommended productlists rank ordered by predicted product performance scores;

[0056]FIG. 10 illustrates in functional form how a productrecommendation engine utilizing a neural network operates in accordancewith an embodiment of the invention;

[0057]FIG. 11 illustrates a cascade of collaborative and content-basedfilters that may be employed in a product recommendation engine inaccordance with an embodiment of the invention;

[0058]FIG. 12 illustrates in functional form how feedback is utilized incertain embodiments of the invention;

[0059]FIG. 13 illustrates in functional form how an exemplaryprofessional only embodiment of the invention operates;

[0060]FIG. 14 illustrates in functional form how an exemplaryprofessional authorized access embodiment of the invention operates;

[0061]FIG. 15 illustrates in functional form how a first exemplaryhybrid professional only embodiment of the invention operates;

[0062]FIG. 16 illustrates a generalized process of a consumerinteracting with an embodiment of the invention;

[0063]FIGS. 17A and 17B illustrate in flow diagram form a process forinteracting with an embodiment of the invention;

[0064]FIG. 18 illustrates in flow diagram form a process for re-trainingthe recommendation engine in accordance with an embodiment of theinvention; and

[0065]FIG. 19 illustrates an exemplary system incorporating anembodiment of the invention and a plurality of revenue stream generationpoints within the system.

V. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0066] Throughout the following detailed description similar referencenumbers refer to similar elements in all the drawings. Also, embodimentsof the invention are discussed and described herein for the most part interms of skin care and skin care products. The invention, however, is inno way limited to skin care or skin care products . Rather, theinvention is broadly applicable to a vast array of target substrates andproduct categories.

[0067] Exemplary Systems

[0068]FIGS. 3 and 4 illustrate first and second exemplary networkenvironments respectively in which the present invention may reside. Ofcourse, actual network environments may be arranged in a variety ofconfigurations and the invention is in no way intended to be limited tothe embodiments depicted and described herein. The environmentillustrated in FIG. 3 is that of a client-server system 300. System 300includes client computers 320, 320 a, which could be personal computers,thin clients, hand-held computing devices, and the like. System 300 alsoincludes at least one server computer 322 and storage element 370 whichis coupled to and controlled by server computer 322. The client andserver computers in system 300 communicate with each other by way ofcommunications network 380, which may comprise any number of networkingtechnologies such as a LAN, a WAN, an intranet, the Internet, and thelike.

[0069] Client computers 320, 320 a and server computer 322 are connectedto communications network 380 by way of communications interfaces 382.Communications interfaces 382 can be any one of the well-knowncommunications interfaces such as Ethernet connections, modemconnections, DSL connections, cable modem connections, and the like.Communications interfaces 382 also may comprise intermediatecommunications networks such as a LAN.

[0070]FIG. 4 illustrates a second exemplary network environment in whichthe invention may reside. System 400 is comprised of the three basiccomponents forming commonly implemented architectures for serving webpages (or in general, Internet connectivity): the public network 401, ademilitarized zone (“DMZ”) 402, and a private network 403. The purposeof separating the private network from the public network is to providea predetermined level of information security.

[0071] In most embodiments of the invention the public network 401 willcomprise the Internet. Because security threats typically reside in thepublic network a software and/or hardware device called a firewall 404is placed along the connection point between the Internet and theprivate network. The firewall 404 blocks all traffic from the publicnetwork to the private network except for predefined types of messagingtraffic such as web access to a defined set of web servers 405. Theportion of the system 400 including the firewall typically is referredto as the DMZ because the resources it contains are only partiallyprotected from outside access.

[0072] In certain embodiments of the invention another firewall 406 isplaced between the DMZ 402 and the private (strictly internal) network403. This second firewall allows access to the internal network onlyfrom machines in the DMZ utilizing a specific predefined type(s) ofmessaging traffic.

[0073] Static data to be served by the system can be placed in eitherthe internal network or in the DMZ. Typically the static data is placedin the DMZ since it is often directly attached to the web servers. Insystem 400 however, the static data is stored on Network AttachedStorage (NAS) 407 which resides in the private network zone. Anadvantage of NAS is that the storage has its own network address andhence the disks can be shared efficiently across multiple computers andis highly scalable. Static data may include, but is not limited to,content served when of interest to consumers, information andinstructions, standard page formats into which individualized data,information and charts can be inserted, and the like.

[0074] Dynamic content (e.g., user-specific information) may also bestored in the NAS 407. However, because dynamic content is likely to bemanaged using a Database Management System (DBMS) such as Oracle or SQLServer system 400 employs a separate DBMS server 408 for dynamiccontent. Use of a separate DBMS server for dynamic content may also benecessary due to the processing requirements involved in themanipulation of data in system 400 and to further support databasescalability.

[0075] System 400 also utilizes a site update subsystem 409. Site updatesubsystem may be used to update the static content of the web site aswell as the content stored in the database. While depicted in system 400as a function dedicated to a single computer, this is a standard website update function and the specific update procedures and locationsare matters of design choice.

[0076] Firewalls 404 and 406 may be implemented as either hardware orcombination of hardware and software. Many firewalls today areimplemented through software running on a relatively small server. SunMicrosystems offers many types of suitable small servers for thisfunction and Check Point Software Technologies, Inc. offers a popularsoftware package having suitable firewall functionality. An alternativeconfiguration of system 400 may be implemented by having the firewallsoftware residing on the small server perform the DMZ function as well,thereby eliminating the need for additional DMZ hardware.

[0077] The purpose of the web switch 410 in the DMZ 402 is to provideload balancing across the multiple web servers 405 within the DMZ 402. Aweb switch 410 may or may not be required in system 400. The selectionof the web switch 410 and web servers 405 are matters of design choiceand numerous vendors offer suitable platforms and packages. Therecommendation engine processors 411 perform the processor-intensiveoff-line calculations needed to generate the individualized productrecommendations. Selection of the recommendation engine processors is amatter of design choice as well.

[0078] In system 300, consumers use client computers 320, 320 a tocommunicate subjective and/or objective data 310 to server 322. Server322 then acts upon and/or stores the consumer data in data storageelement 370. Server 322 uses the consumer data as well as otherinformation stored in storage 370 to generate product recommendations314 (as described more fully below). The product recommendations 314 aretypically delivered over communications network 380 for presentation tothe consumer at the requesting client computer 320, 320 a.

[0079] In system 400, on the other hand, consumers transmit thesubjective and/or objective data 310 to the web servers 405 within theDMZ 402 via the public network 401. The consumer data is thentransferred by the web servers 405 to the various elements within theprivate network 403 as appropriate. The product recommendationprocessors 411, drawing on the consumer data, as well as other datastored on elements within the private network such as the NAS 407 andthe DBMS 408, generate product recommendations. The productrecommendations are then communicated to consumers via the DMZ 402 andpublic network 401.

[0080] The product recommendations typically comprise predicted productperformance and product preference scores for a plurality of productsfor a particular consumer. In certain embodiments of the invention therecommendations are in the form of a custom constructed HTML document, astatic HTML document into which custom constructed text, data graphicsor charts are inserted, and the like. The HTML document also may includeproduct package illustrations and information on the recommendedproducts, as well as radio button options such as “add to my cart,”“sample,” “add to my reminder list”, and the like next to eachrecommended product. A “rate this product” radio button also may beincorporated in the HTML document to permit the consumer to input his orher opinion of recommended products based on their historical use of theproduct. If a consumer has previously rated any of the recommended setpoorly, the system may be programmed so that those products are excludedfrom a future recommended set for that consumer. In some cases theproduct recommendation engine determines whether the set of priorproduct responses indicates likely rejection or downgrading of otherproducts that may be related on some basis.

[0081] Consumers also may request and/or systems 300 and 400 may deliverinformation in addition to the pure product recommendations. Thisancillary information may cover any number of topics related to theproduct recommendations such as the needs of a consumer, and the like.Product recommendations and ancillary information will be discussed ifmore detail below in conjunction with system outputs.

[0082] The bases for the product recommendations may include, but arenot limited to: a) descriptions of product attributes residing withinstorage elements in communication with the data processing orrecommendation engine portions of the invention (e.g., storage element370, NAS 407, DBMS 408, and the like); b) detailed information abouteach characteristic of a particular consumer's interests, including butnot limited to his or her requirements, budget, aesthetic preferences,health needs, and/or need importances; c) other stored historicalproduct data relating to the consumer's purchase patterns and pastresponses to products; d) similar information on some or all otherconsumers who have used the invention; and/or e) the productrecommendation engine (which may utilize a neural network, acollaborative filter, combinations thereof, and the like) which infersthe predicted performance and/or preference for products for anindividual consumer based on the data and processing elements describedabove. Inputs and data incorporated within and utilized by the variouselements of the invention will be discussed in more detail below.

[0083] The accuracy of the inputs to and outputs of the invention may beimproved in a number of ways. One way is to include objective measuresof substrate parameters that correlate with concerns of the consumers.These objective, physical measurements may be used to augment thesubjective inputs (e.g., needs input variables and product performanceratings), or may even displace certain subjective consumer inputsaltogether.

[0084] A second way to improve the accuracy of invention outputs is toperiodically re-train the recommendation engine so that its outputscorrelate more closely with data gathered by the system through consumerfeedback. For instance, where the recommendation engine utilizes aneural network, predictions and actual consumer responses to product useare used periodically to re-train the algorithms residing in the hiddenlayers so that its future outputs (e.g., product recommendations)correlate more closely with the consumer feedback. Similarly, where therecommendation engine utilizes collaborative filtering one re-trainingoptimization routine uses actual consumer feedback to refine the size ofcollaborative neighborhoods used if this is a fixed parameter, adjustthe coefficients that scale each dimension of the collaborative space,and evaluate whether secondary collaborative filtering classificationssuch as performance or preference patterns improve accuracy of outputs.

[0085] Inputs

[0086] Inputs into the invention will now be considered. Query forms maybe used to solicit the various inputs into the system from consumers.FIG. 5 illustrates an exemplary initial query structure. FIG. 6illustrates some of the data that may be included in an individualconsumer database entry within systems employing the invention.

[0087] In the initial or early interactions with a new consumer, theinvention solicits personal profile information (e.g., age, gender,sleep patterns, medical conditions, prescription drug use, knownallergies, geographic location, time spent outdoors, vitamin use, diet,and the like) and target concerns from the consumer. Personal profileinformation is gathered in certain embodiments of the invention becausebased on best knowledge of a category target's area of productsresearch, such information may contribute to generating individualizedproduct recommendations.

[0088] In general, when a consumer interacts with the invention he orshe may be provided the option to either update his or her currentneeds, input product responses, obtain information content or ancillaryinformation relating to a covered area, obtain information on theirresponses to products over time, and/or obtain recommendations forproducts in one of the categories covered by the invention. The latterthree options are invention outputs and will be considered below.Consumer needs data is an invention input and may comprise subjectivedata about the condition of a target substrate. Subjective consumerneeds data may also be augmented and/or supplanted by objectivemeasurements gathered with diagnostic tools. Product responses are alsosystem inputs and may comprise subjective and/or objective dataregarding response of a substrate to whatever product the consumer isusing whether previously recommended or not.

[0089] Upon receipt of product responses from consumers, therecommendation engine or processing associated therewith performs anynumber of operations on or with the data. For instance, and by way ofexample only, individual consumer profiles may be updated to include theobjective and/or subjective data, modifications to various processinginputs such as consumer needs data may be calculated and implemented,and the like. The invention may also use product responses or feedbackto: a) group consumers with similar substrate conditions; b) groupconsumers whose substrate responds better to the same set of products;c) group consumers whose substrate responds by a similar magnitude tothe same set of products; d) measure the effects of products onsubstrate properties; e) compare the effects on a single consumer'ssubstrate to the average effects seen in a population of consumers;and/or i) show consumers any changes that have occurred with use of aparticular product or products over time. The use of feedback isdiscussed in greater detail below.

[0090] Consumers often have product choice biases based on aestheticchoices such as fragrance character or level, product form (e.g., creamvs. lotion, spray vs. rollon, and the like), genre of entertainment,hardcover or soft, and the like. Certain embodiments of the inventiongather aesthetic choice information from a consumer because suchinformation may provide a basis for an operator of systems employing theinvention to check for product preference scatter depending on thesebiases. Where there are preference dependencies among consumers on thebasis of such variables, certain embodiments of the invention may groupan individual consumer with an appropriate class of individuals beforecalculating predicted product preferences and product recommendations.

[0091] In certain embodiments of the invention a consumer may have morethan one concern or problem at a time regarding the target or substratetreated in a category of products (also referred to herein as a categorytarget). Each concern for a particular target substrate is characterizedin terms of the consumer's assessment of the concern's severity and/orimportance. Typically, severity represents a subjective self-assessedlevel of the problem. In some cases however, severity may be assessed byothers, and/or based upon measures of related physical properties orsigns (e.g., diagnostic data). Importance typically is the degree towhich a consumer is bothered or frustrated by the concern. Importancemay also be thought of as extent to which the consumer would trade offbenefits to meet their expectations from a product. (Hypotheticalbenefits of lesser importance would be sacrificed before benefits ofhigher importance.) The magnitude of severities and importances arepreferred consumer characterization inputs for the system. The rankorder of importances may be useful secondary consumer characterizationinputs. Importance and severity are treated as independent factors inpreferred embodiments of the invention.

[0092] A consumer's experience with a product also may be solicited. Incertain embodiments of the invention consumer experiences are recordedin terms of preference and/or performance metrics. Solicitation of bothpreference and performance are preferred. Preference can be thought ofas the answer to the question, “How much did you like using theproduct?” Preference should reflect the user's overall experience, andmay include factors related to any perceived improvement in theconsumer's various concerns, as well as more subjective aestheticfactors such as fragrance, ease of application, flavor, attractivenessof packaging, and the like. Performance should rate the extent to whicha product reduced the signs or other conditions or symptoms associatedwith each concern in a category. While performance may compriseobjective and/or subjective components, the inclusion of objective datais preferred. In situations where multiple products are used incombination by a single consumer, certain embodiments of the inventiondo not distinguish between the relative preference and performanceratings of each product but ascribe the ratings to all the components ofthe set.

[0093] Diagnostic data refers to objective data characterizing the stateof a substrate to be treated by products in a given category. Substratescan be animate or inanimate, including an aspect of the consumer'sperson. In addition to diagnostic data, clients and/or theirprofessional service providers may provide subjective characterizationof substrate conditions and substrate performance responses to productuse. Diagnostic data is obtained from a measurement tool(s) thatmeasures a property related to a concern of the consumer. The propertymay be any physical property of the substrate such as size, mass,mechanical, electrical, optical, and the like. Bulk property examplesfrom each of these categories could be length, weight, stiffness,resistance, opacity, and the like. Other properties relating to asubstrate or feature within a substrate might include position,velocity, acceleration, vibration, rotational velocity, orientation, andthe like. Surface properties of a substrate may include roughness,friction, reflectance, dryness, discoloration, and the like. Diagnosticdata also may be based on chemical analyses. The specific propertiesmeasured by the diagnostic tool(s) will vary depending on the substratebeing considered. Any time dependence of a measured property may be animportant aspect related to a concern or consumers.

[0094] When properties of a substrate being considered are spatiallynon-homogeneous, the range or distribution of the properties may becaptured by taking either randomly located repeated measures (sampling)or location specific measurements (mapping or imaging). Depending on thesubstrate attribute of concern, different functions of the distributeddata correlate with the subjective or perceived attribute of concern.For example, where skin is the substrate and aged appearance is theconsumer concern, wrinkle length could be measured from images of theface. Where the invention utilizes images the diagnostic tool maycomprise a camera, including but not limited to, a digital camera.Diagnostic data for use with the invention may also be multidimensional,meaning a collection of measurements on one or more aspects of thetarget substrate. Collection of multidimensional data is achieved usinga collection of devices, devices having multiple sensors, and/orcombinations thereof.

[0095] Literature in the fields of psychometrics and objectivemeasurements is extensive and may be consulted in formulating newfunctions of single or multiple diagnostic measures that correlate withvarious features of consumer concern and the desired effects of productsin a category. In some cases though, it may be easier to measure theobjective effects on the consumer rather than properties of thesubstrate. For example, where the consumer concern is “comfort of a bed”the relationship may be modeled by a complex function of optimalsoftness of the surface and stiffness of the support. Alternatively, onecould measure directly on the consumer their time to fall asleep orhours of REM sleep and develop a model that relates this to the judged“comfort of a bed.”

[0096] The parameters and/or sets of parameters to be measured with thediagnostic tools must be relevant to issues of consumer interest for aparticular substrate. Multiple parameters can be communicated to thedata processing elements of the invention individually or incombination. Alternatively, multiple parameters measured by a diagnostictool(s) could be combined linearly and/or non-linearly at the clientsite to form an overall functional parameter that is communicated to thedata processing elements on the server side of the system.

[0097] Only as many variables need to be measured as are necessary toformulate a reasonably predictive model of the consumer concern. In manycases, only a single variable selected from a plurality of options isnecessary. Specific parameters and combinations of parameters that couldbe measured where the target substrate comprises skin, by way of exampleonly, include the following. A consumer concern of skin dryness couldinvolve any combination of surface reflectance, redness, skin moisturecontent (capacitance, conductance), skin barrier function (TEWL orchange in moisture following wetting), friction, epidermal hyperplasia,skin flake image analysis, and the like. Skin lesion monitoring couldinvolve any combination of size of specific lesions, color or specificlesions, and the body site of specific lesions. Skin solar exposuremonitoring could involve any combination of basal skin color, pigmentedspot color, contrast, and the like. Visible or ultraviolet light may beused to measure reflectance or fluorescence changes. Skin agingmonitoring could involve any combination of skin color, skin colorevenness, skin wrinkle length or depth, skin sagging, skin rigidity, andskin hydration.

[0098] Other specific parameters and combinations of parameters thatcould be measured where the target substrate comprises skin include:hair color, hair thickness, hair density, hair growth, acne lesioncounts, acne lesion color, acne lesion rate of change, hyperpigmentationsize and area, hyperpigmentation count, hyperpigmentation color,cytology of surface corneocytes (size, shape, and/or nucleation),electrical conductivity, capacitance, mechanical stiffness in the planeof the surface and/or perpendicular to the surface, friction,characterization of the fluorescence of skin, optical reflectivity as afunction of color, microbial detection, optically based determination ofdistribution of pigmentation or photodamage, surface energy by contactangle, 3-D contour determination of sagging or bags under the eyes,redness, discoloration, and/or wrinkle depth and/or length.

[0099] Implementations of the invention incorporating objective data,like that discussed above, typically involve the use of diagnostictools. These embodiments of the invention use diagnostic tools to obtainobjective measurements that help dimension the needs levels of consumers(system input) and/or track the responses of a substrate to a particularproduct (performance feedback). An example of the former is objectivedata being used to adjust a consumer's subjectively assessed concernseverity in one or more concern areas. Diagnostic tools typically arelocated at the client side of the communications network at a consumer'shome, a service center accessible to the consumer, a physician's office,and the like. Utilization of diagnostic tools improves systemsensitivity to effects of the products and provides objective data onthe condition of the target to be treated by the product. The diagnosticdata obtained with a diagnostic tool is communicated to therecommendation engine via communications network 380 (in system 300),public network and DMZ (in system 400), or other similar means ofcommunication. The diagnostic tools and/or sensors employed therein toobtain the diagnostic data may require periodic calibration to assurecontinued accuracy. Calibration can be internal to a device, involveautomatic adjustments when a calibration substrate is used, or beperformed manually.

[0100]FIG. 7 illustrates in functional form how certain embodiments ofthe invention 700 may operate when diagnostic data is incorporatedtherein. Block 701 represents product attribute data gathered by thesystem of the invention 700 (or in the case of initial system startup,entered as priming data). Block 702 represents consumer needs data,objective and/or subjective feedback (such as diagnostic data), personalprofile information, and the like solicited or gathered by the systemfrom consumers using system 700. Arrow 703 represents the operation ofthe system's product recommendation engine (also referred to herein asthe forward intelligence engine) on the system inputs (i.e., blocks 701and 702 information). Block 704 represents the product recommendationsgenerated by the product recommendation engine in arrow 703 and output709 to consumer users of system 700. Block 705 represents the selection,purchase, and use of a product to treat a concern by consumers. Note, asa general matter the product selected and used by the consumers need notbe one of the products recommended by the system 700, or even presentlywithin the knowledge base of the system 700. Consumers may select anduse any product they choose to treat a concern for which they haveidentified to the system 700 (e.g., block 702) and provide feedbackabout that product (e.g., 706, 707, 708). Block 706 represents thesolicitation 710 of diagnostic measurements from the consumers. Blocks707 and 708 represent feedback (e.g., new diagnostic measurements andsubjective responses) received by the system 700 from the consumers andincorporated 712 within the knowledge base of system 700. Block 705 alsorepresents the receipt and consideration of ancillary information output(e.g., diagnostic levels and trends) generated and delivered 711 bysystem 700 from the consumers.

[0101] While networked diagnostic devices have been described in theprior art, data collected by such devices has not been used in a productrecommendation system. In the forward or recommending aspect of thesystems for instance, the state or condition and any historicaldiagnostic responses of a substrate measured with the devices may beused to generate product recommendations. In the reverse or re-trainingaspect of the systems, the objective measurements of substrate responsesto products may be used to re-train the product recommendation engine,which may include product attribute refinement, and/or to updateconsumer profiles. Compared to certain subjective assessments,measurements obtained with diagnostic tools often provide earlier andmore accurate assessment of the effects of product usage. Objective datagathered with diagnostic tools may also reveal changes in importantphysical phenomenon otherwise unavailable to a consumer (e.g., changesin density of bacteria in or on a substrate, changes in pH level of asubstrate, and the like).

[0102] For a personal or health care category the diagnostic measurementor multiple measurements might monitor internal or external propertiesof the consumer or the environment. For other categories of products thediagnostic tool may measure the condition or performance of an inanimateobject. Ideally, the tools are made available at all times at the clientsite. The tools can output values that a consumer sends to the inventionby email, data entry section of a web site, telephone, or the like. Theuse of diagnostic tools that are interfaced with a client computer orother electronic device located at a remote site (e.g., client site,kiosk location, professional service provider office, consumer servicecenter, and the like) for automatic downloading of consumer data to theinvention is preferred. In embodiments of the invention where adiagnostic device is remotely located from the data processing portionsof the invention, the data processing portion of the system may performall or most high level processing of the device's output to reduce thecost of the remotely located device. Diagnostic tools also frequentlyincorporate a standardization process.

[0103] Outputs

[0104] Attention will now be focused on outputs of the system of theinvention. The primary output of the invention comprises individualizedproduct recommendations in a particular category and regarding aparticular substrate. In most embodiments of the invention, the productrecommendations attempt to address all of a particular consumer'scurrent concerns, and take into account both the severity and importanceof each concern. Typically products are recommended based upon all knowneffects of the product and the consumer's interest in addressing a rangeof problems and tolerance or sensitivity to adverse effects, which arejust additional rows in the concern matrix.

[0105] While certain embodiments of the invention explicitly recommendsets of products in which each product in the set is a specialist for aspecific concern, the product recommendations generated by mostembodiments of the invention are comprised of products in a categorythat best address all of a particular consumer's concerns, preferablytaking into account their severity and importance. The latter approachis feasible because individual products are either designed orinadvertently affect many of the conditions (e.g., acne and oily skin)characterizing targets, and products generally address underlyingproperties or processes that have broad effects across concerns. When aproduct is part of a “packaged set” of products intended to be used incombination, certain embodiments of the invention make recommendationsfor such “packaged set” products as part of all categories that thepackaged set covers.

[0106] With embodiments of the invention incorporating objective data, aparticular consumer who does not provide objective data on his or herconcerns, preferences, and/or the performance of products used may stillobtain product recommendations based on his or her concerns andsubjective preference and performance data. Such recommendations stillbenefit from the data gathered by the invention from other consumers. Inthese embodiments such users may or may not have access to otherfeatures of the invention based on or requiring objective data.

[0107] Underlying the individualized product recommendations are aplurality of personal utilities generated by the product recommendationengine for a given consumer. The personal utilities are unique and mayinclude, by way of example only, scored predicted product performanceutilities and scored predicted product preference utilities.

[0108] For scored predicted utility scores any number of meaningfulscales may be used. In certain embodiments of the invention however, ascored scale of 1 through 10 is used where 10 represents the highestlevel of preference or performance and 1 represents the lowest level ofpreference or performance. The highest level of preference orperformance is set by the system administrator in each assessedpreference or performance category and may be unattainable by currentproducts on the market. For example, in terms of performance for skincare products, in the area of moisturization a 10 might represent a highchange in moisture levels in a single day while a 10 might represent a50% improvement in 4 weeks in the area of wrinkle repair. Alternatively,the reported score may have a value that correlates with magnitudes aconsumer uses for subjective assessment of product performance orpreference. Certain embodiments of the invention utilize overallpredicted performance utilities, which are detailed below in connectionwith data processing and the product recommendation engine.

[0109] The predicted personal utilities may be used by the system invarious ways including, but not limited to, producing any one or more ofthe following: a rank order listing by utility of product options; arank order listing by utility for a top-N set of products; a rank orderlisting by utility for a number of arbitrary products of interest to theconsumer; a bar chart of utilities for products of interest; a personalutility score in a comparison display showing specifications on a numberof products of interest to the consumer; and the like. Performanceattributes for any set of selected or recommended products may beplotted versus the product price, including overall performance of theproducts.

[0110]FIGS. 8A and 8B are exemplary output displays of rank orderlistings for a top-3 set of products by scored predicted preference.FIGS. 9A and 9B are exemplary output displays of rank order listings fora top-3 set of products by scored predicted performance. Note that eventhough the displays illustrated in FIGS. 8 and 9 are rank ordered bypredicted preference and performance, respectively, each of the displaysalso present predicted performance and preference respectively for eachproduct in the display. Both utilities need not be presented toconsumers together in the same display. Note also that the displaysdepicted in FIGS. 8 and 9 include a lowest known price for each productlisted. Presentation of this information is optional.

[0111] Another output provided by the invention may comprise ancillarydata or information. Displays of a consumer's condition based on thediagnostic and/or subjective data collected by the system maycommunicate to the consumer how the consumer's needs compare to arelevant population, the awareness of the system of their specificneeds, how they have responded to specific products over time, and thelike. Graphics may indicate which products were being used by a consumerat different periods of time as well as trend data.

[0112] Another form of ancillary information output comprisesexplanations of why certain products were recommended. For example,performance prediction metrics may be explained by querying theinvention with a radio button or other appropriate interface and theinvention could respond by providing a table of concern areas ordered bythe consumer's importance and/or performance scores for the particularproduct being considered for each area of concern.

[0113] Where the product recommendations generated by the invention aredelivered to a consumer who has Internet connectivity, the consumer mayact on the recommendations by ordering or purchasing one or more of therecommended products via the Internet. For instance, links to anordering function incorporated in the invention or to another possiblyallied distribution company may comprise ancillary information outputfrom the invention.

[0114] Ancillary information output also may include: links to productreviews from consumers with similar substrate needs; directed contentbased on the consumer's problem segmentation; information regarding thecondition of a consumer's substrate within a historical framework toshow responses to use of the invention or use of particular products orproduct classes or periods of any particular underlying behavior;information regarding the condition of a consumer's substrate relativeto peers or a relevant population (demographic positioning could bereported within local geographic or limited ethnic or age limits);information regarding changes resulting from new product usage orregimens through images with or without sensitivity enhanced relative togeneral perception; information regarding the dependence of results onusage practices such as frequency of use or use of associated productsor practices; comparison information that aids a consumer insubjectively assessing performance of a product (e.g., before and afterimages of a substrate of concern); and the like. Other ancillaryinformation could include interactive and/or multimedia applicationsand/or displays. For instance, an interactive and/or multimediaapplication could: guide a consumer through the proper use of arecommended or selected product; aid a consumer in assessing theperformance of a product; and the like. Interactive and/or multimediaapplications and/or displays may include text-based chat rooms,video-based chat rooms, streaming media, virtual help, agents,interactors, and the like.

[0115] Many targets, for example health based substrates (and inparticular the skin) change and age at slow rates. Other targetsubstrates also may respond to products over relatively long timetables.Therefore, over short time frames the desired effects of products may besubtle and difficult to discern. Thus, another form of ancillary outputthat may be provided in certain embodiments of the invention areprogress indications. Progress indications, where possible, may enableconsumers to better judge product performance, provide an aid to memory,provide earlier decision making on product suitability, encouragecompliance with effective products, and/or discourage continued use ofineffective products as soon as possible.

[0116] With progress indicators, as a consumer uses a product he or shemay view the effects of the product on the category target (e.g., skinproperties, lung functions, and the like). Tracking may be provided forindividual conditions or for an overall condition, in analogy to theoverall performance discussed below. Where provided, condition iscalculated from data that is being tracked at home or with the help ofprofessional service providers. In some cases, a condition may be basedon subjective feedback. For instance, the data may include subjectivefeedback on current severity of each concern of the category, it may bebased on a set of questions about assessable attributes of the target,and/or utilize objective diagnostic measurements. Preferred embodimentsutilize at least some form of objective diagnostic measurements. Foreach of i concerns, the data processing architecture of the inventionproduces a current condition_(i). The condition_(i) may be any model ofthe data obtained by the invention related to concern_(i).

[0117] Preferred embodiments of the invention provide progressindicators because communicating any change in a condition together withtypical or expected changes helps build realistic consumer expectationsof product performance and effects. Possible displays include a curverepresenting the average change in condition for consumers starting withcondition levels similar to the given consumer, and data points or acurve fit to the data for the given consumer. To illustrate, the typicalchange curve for consumers within a normal range of that condition mightbe flat, whereas a typical change curve for consumers one sigmadeviation from normal might show improvement occurring over a certaintime scale (t_(½)).

[0118] Data Processing

[0119] The details of the forward intelligence or product recommendationengine will now be provided. As discussed above, one of the possibleoutputs of the invention comprises product recommendations. Theinvention generates its recommendations through the use of a productrecommendation engine that performs multivariate modeling and analysisof the independent variable inputs it receives from consumers. Dependingon the embodiment of the invention implemented, the productrecommendation engine may utilize any combination of the inputsdiscussed above to produce any combination of the outputs discussedabove. In certain embodiments of the invention, the productrecommendation engine utilizes one or more neural networks to generateoutputs from the inputs. In other embodiments of the invention, theproduct recommendation engine utilizes a collaborative filter orcombinations of multiple collaborative filtering models to produce itsoutputs. In still other embodiments of the invention, the productrecommendation engine utilizes combinations of neural networks andcollaborative filtering to process the system inputs.

[0120] Data Processing: Collaborative Filtering

[0121] Any collaborative filter has at least three main elementscomprising data representation, neighborhood formation function, andrecommendation generation functions. Each will be discussed separately,beginning with data representation.

[0122] Certain inputs and outputs are numerically represented for use bythe product recommendation engine. As discussed above, products addressthe needs of consumers and in general have a range of types of effects.Consumers communicate concerns in a product area in terms of theseverity of their needs (or sensitivity to adverse effects) and howimportant they consider these concerns to the product purchase decision.Depending on the target substrate, a plurality of concerns may berelevant. In certain embodiments of the invention concerns are presentedfor the consumer to choose from. This simplifies the dimensionality ofthe consumer's interactions with the invention to just those concernsthey have chosen to address.

[0123] Exemplary concerns, where the category is cleansers and thetarget substrate is skin, might include cleaning dirt, cleaning grease,killing bacteria, irritating skin, drying skin, imparting a tighteningfeeling, leaving skin feeling soft, lathers easily, economical, smellingpleasant, and the like. In general, there are i concerns in a givencategory. While the target of the category may be the only substrateconsidered in concerns, multiple substrates could be involved. Forexample, in this category the main substrate is skin, but how theproduct leaves a film on the tub addresses a secondary substrate.Associated with each of the i concerns is an importance level. A newconsumer (or an existing consumer wishing to revise his or her personaldata) interacting with the invention typically identifies concerns,levels for each of the concerns, and how important each concern is inthe product selection decision. A new consumer may also be asked toidentify sensitivity to adverse product effects. An aided scale, wheresome input values are described by words and/or pictures, may beutilized in the data collection process. Other input devices such asslides or dials or image synthesis and the like are envisioned.

[0124] A user's Concerns are represented by a N×2 matrix, _(u)C_(ik),where: u is an index representing the consumer (which may be dropped forconvenience in the following); N is the number of Concerns recognized bythe invention; i=1,N; k=1,2; C_(i1)=Severity of i^(th) Concern,0<=_(u)C_(i1)<=10; C,_(i2)=Importance of i^(th) Concern,0<=_(u)C_(i2)<=10. Each concern Ci is typically known to the user by adescriptive text name (e.g., wrinkles, dryness, pimples, and the like).A 0 to 10 scale used for each element of the concern matrix is, ofcourse, arbitrary.

[0125] Although concern severities and importances are generallysubjective assessments, in the case of certain concern areas (e.g.,where the quality of subjective information is notorious) the subjectiveassessments may be augmented and/or supplanted with objective data. Incertain embodiments of the invention scaled objective data (e.g., ameasured variable) may stand in for a concern or a concern input may berestricted or modified by virtue of the objective data. For example,where skin is the target substrate, skin roughness, photodamage index,and/or elasticity (turgor) may be relevant to scaling certain problemseverities provided by a consumer. The objective data and/or scaledvalues may be used to extend the range of concerns, to add a new concernto the category target concerns, or modify existing concern severityvalues (e.g., by using a linear combination of the subjective andmeasured values related to a specific concern). For example, a consumermay assess wrinkling severity and the invention may weight theconsumer's subjective assessment by a machine value for total wrinklelength. Data from within a consumer's personal profile such as age ortime outdoors may also provide relevant information and be used toadjust concern parameters.

[0126] Numerical representations of each measured parameter are chosenso as to reflect the method of measurement in a convenient fashion. Eachseverity score, C_(i1), is a real number in a predefined range, forexample, between 0 and 10. The computation that relates measurements orrelevant signs or other material properties to C_(i1) is structured sothat the most severe case of concern C_(i) is given the highest score(e.g., 10), while the lowest score (e.g., 0) corresponds to a lack ofany reported or observable signs or physical indications for theconcern.

[0127] The importance of concern C_(i), called Ci_(i2), is representedas an integer in a finite range, such as the range 0-10, and the like.Note that in preferred embodiments of the invention importance valuesare not normalized because relevant information is often contained intheir absolute magnitudes and should be preserved.

[0128] Turning to representation of preference data, as consumers useproducts they provide preference feedback on those products. Inaddition, new consumers may provide preference values for products theyhave used in the past, for example in their initial interaction with orearly in the process of starting to use the invention. As discussedabove, preference is a measure of how much the product is liked. Avariety of aided scales may be provided. Table 1 shows an exemplaryaided preference scale. TABLE 1 1 2 3 4 5 6 7 8 9 10 very worse averagebetter best inferior than than most most

[0129] All aspects of a product typically impact a given consumer'spreference for that product, including how well the consumer thinks theproduct works. In other words, preference values may be influenced bythe aesthetics of a product, the perceived performance, and/or marketinginformation. Consumers may also be influenced by system participation,potential invention outputs such as the recommendation of a product onthe basis of predicted preference, predicted performance, and/orperformance tracking information provided to consumers. For this reason,in preferred embodiments of the invention once a new consumer hasevaluated five or six products recommended by the invention his or herinitial or pre-invention use preference values are eliminated from thatconsumer's preference pattern data.

[0130] A consumer's preference score for a given product, _(up)PREF,(u=user index, p=product index) is represented in the system as aninteger in a finite range, such as the range 0-10, or 0-100, and thelike. A consumer's preference score for a given product also shouldcorrespond to the rank-ordered set of preference descriptors.

[0131] Where the basis for collaborative filtering is finding aneighborhood of similar product preference patterns, it is the patternof preferences across a range of overlapping products used by consumersthat determines similarity among consumers. To improve predictions ofproduct preference, certain embodiments of the invention applypreference based collaborative filters after concern based collaborativefilters are applied to subset the population to those with similartarget problems.

[0132] Representation of target or substrate conditions is nowconsidered. Performance predictions and feedback are derived from datathat tracks individual conditions of the target substrate for a givencategory. In preferred embodiments of the invention each condition is anobjective index of the current properties of the target substrate thatrelate to each of the concern areas. Condition values are ideallycalculated from a set of primary measurement variables. However, in somecases condition values may be based on a combination of one or more ofsubjective feedback on current severity of concern of the category(e.g., itch where the target substrate is skin), subjective feedback ona set of questions about specific assessable attributes of the targetsubstrate (e.g., number of cracks on the hands, the minutes of skinfeeling tight, and the like where the target substrate is skin), andobjective diagnostic measurements. For each of the i concerns, dataprocessing produces a current condition i. Conditions may be any modelof the data obtained that reflects concern. Whether the model correlateslinearly with perceived severity is not necessarily relevant. Toillustrate the concept further, consider tires as a product category.Concerns related to tread wear could be mileage or remaining tread.Measurements of tread depth and odometer miles could be converted toconditions of the tire related to the concerns as follows: tire mileagecondition=(change of odometer miles)*(original tire tread depth)/(changeof tread depth); remaining tread (in miles)=milage*(remaining treaddepth-minimum safe tread depth)/(original tread depth). Conditionscharacterize the state of the target in a way that directly relates tothe consumer's concerns.

[0133] When characterizing performance of a product with preferredembodiments of the invention, all conditions data obtained while aconsumer is using a product is stored until the use of the product iscompleted and the performance feedback of the consumer for the productis decided. The initial and final values of the conditions data arestored as a part of the consumer databases in preferred embodiments ofthe invention as well.

[0134] Representation of performance, overall performance, andperformance pattern data within the invention are now considered. Asdiscussed above, conditions are characterizations of a target orsubstrate at a point in time. Since starting use of the product, thechange or rate of change of Condition_(i) are possible measures ofperformance. The precise scale used to numerically represent such dataoften depends on the characteristics of the particular data beingconsidered. In preferred embodiments of the invention however, apositive or negative 0-10 performance scale is employed. In certainembodiments of the invention a predetermined value of change is assignedto particular values on the performance scale. For example, where a 0-10performance scale is utilized a predetermined value of change may beassigned to the values 0, −5, −10, 5, and 10 on the performance scale.

[0135] The change of condition is the difference in the value ofcondition averaged over readings obtained in some fixed time intervals.Exemplary intervals may be the two weeks preceding start of product useand 8 to 12 weeks after start of product use. The precise intervals forsampling and initial product effects assessment selected however willnecessarily depend on the product category and associated target. Inpreferred embodiments of the invention the change of condition value isthe difference between the value of condition averaged over a samplingtime interval beginning some defined time period after start of use lessan initial condition (which may or may not be averaged over some initialtime interval, perhaps during the week preceding start of product use).Rate of change of condition could be the fit slope of the condition oversome defined time interval, for example the first two months of productuse. The target substrate's change kinetics, typical product effectkinetics, and the specific condition are factors that may be consideredin deciding how condition changes are translated to a useful performancescore.

[0136] Once appropriate time intervals are selected, an appropriatetranslation function is selected to arrive at a performance score.Translation functions may comprise any number of functions including, byway of example only, linear translation by formula, nonlineartranslation by formula, and/or a lookup table. The process of selectinga translation function may begin by looking at the distribution ofcondition changes across all products and a large number of consumers sothat the distribution of changes may be translated to performancescores. An exemplary translation may have the top 10% of changescorrespond to a 10 on a performance scale of 0-10 while average resultscorrelate to a 5. A tool helpful in performing the foregoing is a twodimensional map of distribution of condition changes as a function ofinitial condition. A model of performance score based on amount ofchange and initial level of a condition is often preferred.

[0137] Performance of a product for a given consumer preferably iscomputed for each concern Ci using the observed change of the conditionand the level of the consumer's initial concern, _(up)PERF_(i), (where‘u’ and ‘p’ are indices referencing the consumer and productrespectively and i is the condition index). The overall performancescore for the product, _(up)PERF, typically is computed as theimportance weighted sum of the each concern Ci using the observed changeof the condition and the level of the consumer's initial concern,_(up)PERF_(i), (where ‘u’ and ‘p’ are indices referencing the consumerand product respectively and i is the condition index), normalized bythe sum of importances.

[0138] In the case of biological target substrates, measurable orassessable signs are an alternate terminology for certain properties ofthe target substrate. In this case change in condition is determined bychange in the signs. Optionally, performance metrics at the level of thesigns can be utilized with the invention.

[0139] For a particular consumer, an overall performance prediction maybe presented as part of the product recommendations output. Overallperformance prediction may characterize the “predicted performance” of atop-N performance recommendation listing output, and also may bereported in other formats of recommendation outputs. Overall performancepredictions use the predicted performance for each concern derived fromthe observed performances seen by consumers similar to the consumer(such as those in the consumer's collaborative neighborhood, in certainembodiments of the invention). The overall performance prediction is theconsumer's concern importances (C_(i2)) weighted average of the productperformance predictions (normalized by the sum of the importances.)

[0140] In certain embodiments of the invention, a performance responsepattern comprises a rank ordering of product performance results in asingle concern area or overall for all the products the consumer hasused and provided feedback to the invention. This rank order allowsgrouping of clients with similar response patterns versus differentresponse patterns. In certain embodiments of the invention separation ofthe population of consumers into different response pattern classes isperformed when doing so reduces the standard deviation of performancefor that concern for the subpopulations versus the entire population. Ifperformance response pattern is a valid predictor of performance,consumers may be clustered or a secondary collaborative neighborhood maybe defined on the basis of performance response pattern for the set ofrelevant products. This is done to separate targets with common responsemechanics from targets having alternative response mechanics.

[0141] Personal profile information may be represented numerically also.As discussed above, personal profile information may be relevant to thecondition and concerns of a target substrate in a particular productcategory. A consumer personal information vector, _(u)PI, may beconstructed whose components correspond to the personal profile data ina fashion that will enable _(u)PI to be used in computing or filtering asimilarity group (also referred to herein as a collaborativeneighborhood).

[0142] Psychographic or personality markers may also be assessed andrepresented numerically as they are often determinants in productpurchase decisions and may manifest effects on product preferences.Psychographic markers are more likely to be advantageous in embodimentsof the invention employing neural network analysis than collaborativefiltering.

[0143] The second main element in collaborative filtering isneighborhood formation. Techniques for defining a subgroup of consumersthat are similar to a given consumer are now defined. Any set of datathat will be used to establish consumer neighborhoods comprises a spacethat is a multidimensional representation of the consumer population. Inanalogy to the distance between two points in ordinary space, ageneralized distance between any two consumers (consumer j and consumerk) in the relevant space is defined as:

d _(jk)={square root}(Σ_(i)a_(i) ²(ΔP _(ijk))²)/{square root}(Σ_(i)a_(i)²)  (Eqn. 1)

[0144] where ΔP_(ijk) is the difference in consumer j and k's values ofthe ith parameter used in the consumer space, and a₁ is a coefficientthat scales the various parameters. In preferred embodiments of theinvention the distance is normalized. Normalizing stabilizes thedistances as different parameters are considered as part of the consumerspace and coefficients are scaled, stretching the space in various waysto test for narrower prediction distributions in spherical neighborhoods(all consumers within a fixed distance from a particular consumer). Thiscriteria is equivalent to improving the precision of predictions. Thesmaller the distance used to limit the size of the neighborhood thetighter the prediction distribution. On the other hand, the smaller thedistance the fewer values there are for a given product, thereby runningthe risk of hurting accuracy. The coefficients a_(i) should be chosen sothat all dimensions (adjusted parameters) have similar effects on thespread in prediction distributions.

[0145] Ideally, “similar” means consumers that have similar preferenceand performance outcomes to product usage. In some embodiments of theinvention the definition can be directly implemented so that theparameters P in Eqn. 1 include the performance and preference scores forproducts and the distance is based in part on the similarity of theseparameters wherever there is usage data for a common product. In thisscheme, the distance between any two consumers involves a differentnumber of dimensions. The normalization denominator in Eqn. 1 canaccount for this. However, the number of shared products should berecorded and when there are not adequate overlap in product use toestablish a confident determination of similarity, the other consumershould be excluded from a given consumer's neighborhood. It isbeneficial to allow more consumers in the collaborative neighborhood sothat more product recommendations can be made with greater confidence.For this reason, a preferred embodiment of the invention uses similarityin concerns and other consumer characterizations to establish a sizable,relevant neighborhood or recommenders.

[0146] In this model, all consumers are available for membership inneighborhoods because usage of products in common is not required. Theparameters used to position each consumer in a space are formed fromcombining any or all of concern severities, concern importances, targetconditions, personal profile information, and aesthetic choices.Dimensions of the space may also be constructed from various ratios orproducts of these consumer characterizations. The similarity group for agiven consumer user is then defined as the set of other consumers withina limiting distance from the consumer being served in this spaceaccording to Eqn. 1. The limiting distance may be adapted for eachconsumer and product being considered for predictions of performance andpreference, so that a statistically appropriate number of “similar”product users are captured. For example, as the number of system usersgrows, the threshold could be reduced while maintaining the same averagenumber of similar consumers. When a preference or performance basedsimilarity dimension can be constructed this can be combined with theconsumer characterization based space described herein.

[0147] Aesthetic choices, discussed above, are a subset of personalprofile information concerned with preferences for specific forms ofproduct within a category. Preference patterns often are stronglyinfluenced by one or more aesthetic choices. Thus, in certainembodiments of the invention a consumer's predicted preferences arederived at least in part from a neighborhood of consumers additionallyfiltered to have the same or similar aesthetic choices. For example,where the product category is cleanser and the target skin, a consumerwho prefers bar soap over a shower gel would not be interested in ashower gel preference-based recommendation. The invention nonethelessmay still present a shower gel in a top-N performance list because aconsumer may still want to know that a high performance predictedproduct is out there even if in a less desired form. A neighborhood maybe defined so that it is large enough to generate all of therecommendation values (predicted performances and preferences) that areneeded for a session with a particular consumer. In certain embodimentsof the invention a neighborhood limiting distance can be a constant forany given consumer. In preferred embodiments of the invention though alimiting distance can be selected for each consumer based on the densityof consumers in the region of the consumer space where the particularconsumer is located. The latter embodiment allows a larger limitingdistance to be selected when a consumer is located in a region of theconsumer space that is sparsely populated. Even more precision in thepredictions may be achieved if the limiting distance is adjusted foreach consumer and for each product considered so that every predictionuses the smallest possible limiting distance. There are computing costsassociated with the foregoing and subtle effects on the precision ofcomparisons because a different group of consumers is involved for eachproduct's predictions. Products without sufficient statistical support(too large a variance or too few instances of use) may be culled.

[0148] The consumer space may be constructed in a number of ways, and asingle embodiment of the invention may use one type of space to generateproduct recommendations for certain consumers and other types of spaces(e.g., using different parameters) to generate recommendations for otherconsumers. Commonly, the space for preference predictions will involvedifferent dimensions than the space used for performance predictions.Note though, in each case the space should be filled with every clientin the system. Exemplary though narrowly defined spaces that may beemployed in the invention include, but are not limited to: needs-basedspaces, responsiveness spaces, preference spaces, and the like. In eachcase, the space can involve dimensions that go beyond the titlelimitations. Furthermore, the use of compound spaces which add thecharacteristics of two or more of these simple spaces are within thescope of the invention.

[0149] In a needs-based space, concern severities may be the majority ofdimensions. Another space may be formed with the product of importanceand severity data, which may be referred to as a needs gap space. Eachspace may add personal profile dimensions or any other parameters thatimprove the quality of predictions when compared to feedback.

[0150] A responsiveness space is based primarily on conditions orchanges in condition recorded for standard products or classes ofproducts. A responsiveness space might be employed where the targetsubstrate is medical in nature and it would be helpful to groupconsumers. Response patterns are described above. In the case of medicaltargets response patterns identify consumers on the basis of underlyingbiological mechanisms.

[0151] A preference space may be particularly useful for a productcategory where a relatively large number of products can be sampled andconsumers can provide definitive preference information. Where consumersalso provide objective feedback on the specific performance factors,detailed performance predictions can enhance consumer purchase choices.An automobile product category is well-suited for use of a preferencespace. A preference filter may let a consumer who likes certain types ofvehicles, best expressed by the set of vehicles that fit this class, seethat other consumers with similar tastes also like a few models theclient is not familiar with. Real world consumer generated data onaspects of vehicle performance that could aid the consumer in selectingone or more of the recommend vehicles would be provided in certainembodiments of the invention.

[0152] The third main element of a collaborative filter comprises therecommendation generation function. Given a consumer and the set ofsimilar consumers (i.e., neighborhood), product recommendations for thegiven user are made. Once the dimensions or coordinates of thecollaborative consumer space are selected and the size of thecollaborative neighborhood defined, preference and/or performance scoresare calculated for every product in the category for the given consumer.The scores may be sorted, and then used to define a top-N list ofpredicted preference products, where N is the number of productrecommendations presented to the consumer.

[0153] Overall performance prediction scores often are more complicatedthan preference scores because they are generated from a performancematrix, a one dimensional matrix for each product predicted for eachconsumer. There is a performance component for each concern topic. Foreach product in the category, the average performance matrix iscalculated over all consumers within the collaborative neighborhood.Typically, filtering is not done for aesthetic choices. Filteringhowever may be performed for other personal profile information factorsused in the preference prediction. In preferred embodiments of theinvention overall performance for a single product for consumer k(OPP_(k)) is the consumer's importances (I_(i)) weighted average of theelements of the performance matrix. Or, in equation form:

OPP _(k)=Σ_(i) I _(ik) P _(i)/Σ_(i) I _(ik)  (Eqn. 2)

[0154] Overall performance scores may be sorted, and then used to definea top-N list of predicted performance products. In preferred embodimentsof the invention the OPP scores are reported for products in a top-Npreference table as well (when available).

[0155] Data Processing: Neural Network Analysis

[0156] Certain embodiments of the invention utilize a neural network togenerate its recommended products. The neural network is used to modelthe relationship between various inputs, such as consumercharacterizations and consumer feedback, and various outputs, such asproduct performance and preference predictions. Each consumer typicallyhas a range of one or many needs to be addressed.

[0157] The input variables may include client personal profileinformation, preference and performance values for previously usedproducts, concern matrices (typically including severities andimportances), and conditions which are psychometric models ofassessments or measurements of target attributes that relate to concernareas. As consumers use products recommended by the invention, theirindividual preferences data and performance matrices for products usedaccumulates additional data.

[0158] As discussed above, given a consumer's set of input parameters,inputs from other consumers who have used and provided performanceand/or preference data, and a trained neural network, the productrecommendation engine uses the neural network to generate predictions ofperformance and/or preference for products which have been used by otherconsumers, but not necessarily by this consumer. Product recommendationoutput forms for the given consumer (typically in the form ofperformance and preference predictions contained in custom constructedrecommendation tables) are easily generated from the sorted predictions.

[0159] In preferred embodiments of the invention the predictedperformance score comprises an overall performance score derived from aperformance array for each product recommended to a consumer. Typicallythere is a performance prediction for each concern identified by theconsumer. For each product in the category, the performance matrix isoutput based on the neural network's model for each performanceparameter. The overall performance prediction (OPP_(k)) for a singleproduct for consumer k is computed in the same manner as with acollaborative filter, discussed in detail above. Overall performancescores may be sorted, and then used to define a top-N list of predictedperformance products. In preferred embodiments of the invention the OPPscores are reported for products in a top-N preference table as well(when available).

[0160]FIG. 10 illustrates in functional form how the productrecommendation engine 1000 operates in an embodiment of the inventionthat utilizes a neural network and the neural network utilizes productattributes as inputs. Product recommendation engine receives as inputsproduct attributes 1001 (derived from all the system knowledge aboutproducts used by consumers or, upon startup of the system, priming data)and an individual consumer's characterizations record or profile 1002.The processing or hidden layers 1003 of operate on the inputs 1001, 1002to produce product recommendations outputs 1004.

[0161] Data Processing: Hybrids

[0162] Where the recommendation engine of the invention utilizescollaborative filtering as described above, neural network analysis canbe used to improve the function/performance of the productrecommendation engine. For instance, and by way of example only, theoutput (e.g., predictions) from the collaborative group can be processedby a neural network, or a neural network may be used to generate earlypredictions of whether a product is likely to not be beneficial.

[0163] As discussed above, filtering of collaborative neighborhoods onthe basis of aesthetic choices and/or other personal profile informationmay tighten the standard deviation of the distribution of preferencesaveraged within the collaborative neighborhood to provide a consumer amore accurate prediction. After collaborative filtering to a consumer'sneighborhood in client space, a neural network may be trained to selectthose consumers most likely to match the responses of the consumer beingserved. In embodiments of the invention that periodically examine thequality of predictions, the neural network operating on all availableinputs can find better predictive models for each output parameter. Anembodiment of this invention might use collaborative filteringtechniques for performance prediction generations and neural networkmethods to generate preference predictions. In this variant of theinvention, the predicted performance data could be an additional inputfor the neural network generating preference predictions.

[0164] Yet another hybrid data processing model that may be employedcombines collaborative and content-based filtering. FIG. 11 illustratesa cascade of collaborative and content-based filters 1100 utilized incertain embodiments of the invention. Cascade 1100 represents a novelapproach to exploiting both social and content information that isparticularly well suited to the present invention. With this cascadedarchitecture 1100, the collaborative filter 1102 is tuned to outputpredicted ratings 1103 for many products based on a current consumer'scharacterization profile 1107 and the knowledge regarding all consumersand products contained in database 1101. Ratings outputs 1103 then formthe input to content-based filter 1104, which selects products fromthose inputs for which the product features stored in the productfeatures database 1105 match well with the user's aesthetic choicescontained in the personal profile information. The products selected bycontent-based filter 1104 comprise the final recommendations 1106 outputby the product recommendation engine.

[0165] Data Processing: Database Priming

[0166] New products may be introduced into the system in a variety ofways. Consumers may enter feedback data for a product that is not yet inthe system by entering appropriate product identity information. Thoughthe system will not generate recommendations for the product untiladequate feedback is available, it will continue to accept feedback fromusers. The system may utilize methods to enable faster recommendationsof a new product which we refer to as priming. The product database maybe primed with synthetic and/or actual historical inputs and feedback.In systems using product attributes, that is, performance datarepresenting the mean performance for each consumer segment, the primingdata may incorporate product attributes assigned by experts in thefield. The priming data would be diluted out rapidly by actual feedbackof the new product. Alternatively, performance and/or preference data ona new product could be obtained from recruited sets of wellcharacterized users, or the like. Over time, as consumers use the newproduct, current data is assembled and priming product attributes and/orproduct performance and preference data are adjusted or diluted inconformance with the assembled data. When enough records of system baseduse of a product are accumulated, the priming data may be eliminated asit is likely inferior to the system-based data. Re-training is discussedin more detail below.

[0167] Re-training and Feedback

[0168] Re-training of the product recommendation or forward intelligenceengine will now be considered. Certain embodiments of the inventionimprove their recommendation quality over time by periodicallyre-training the product recommendation engine based on consumerfeedback. In particular, preferred embodiments of the invention utilizepreference and performance ratings received from consumers after usingproducts to periodically assess the precision and/or accuracy of productrecommendations generated by the invention. The data processingalgorithms of the invention are re-trained to reduce the differencesbetween actual feedback and earlier predictions. As the density of dataincreases the optimal weighting functions and spatial structure maychange. In this way, the outputs of preferred embodiments of theinvention continually improve as the population of performance andpreference feedback data grows. Accordingly, another form of ancillarydata output by certain embodiments of the invention may compriserecommended feedback intervals.

[0169] Accuracy is some measure of the agreement of each consumer'spredicted performance and/or preference values with feedback regardingthese parameters from consumers after using the products. Improvingagreement amounts to minimizing the sum of the differences (predictionless feedback), or minimizing the sum of differences squared, and thelike. Adjustments may include changing the spatial dimensions or theirscalar weightings in a collaborative filtering space, filteringneighborhoods by additional personal profile information variables,re-training a neural network, applying better neural network models tothe predictions from collaborative filtering models, and the like.

[0170]FIG. 12 shows in functional form how feedback is utilized incertain embodiments 1200 of the invention. Block 1201 represents productattribute data gathered by the system of the invention 1200 (or in thecase of initial system startup, entered as priming data). Block 1202represents consumer needs data, objective and/or subjective feedback(such as diagnostic data), personal profile information, and the likesolicited or gathered by the system from consumers using system 1200.Arrow 1203 represents the operation of the system's productrecommendation engine (also referred to herein as the forwardintelligence engine) on the system inputs (i.e., blocks 1201 and 1202information). Block 1204 represents the product recommendationsgenerated by the product recommendation engine in arrow 1203 and output1209 to consumer users of system 1200. Block 1205 represents theselection, purchase, and use of a product to treat a concern byconsumers. Note, as a general matter the product selected and used bythe consumers need not be one of the products recommended by the system1200, or even presently within the knowledge base of the system 1200.Consumers may select and use any product they choose to treat a concernfor which they have identified to the system 1200 (e.g., block 1202) andprovide feedback about that product (e.g., 1208, 1212, 1216). Block 1208represents feedback (e.g., new diagnostic measurements and subjectiveresponses) received by the system 1200 from the consumers andincorporated 1212 within the knowledge base of system 1200. Arrows 1215and 1214, together with block 1213, represent the re-training (sometimesreferred to herein as a reverse intelligence engine) of the system's1200 product recommendation engine (product recommendations 1204 arecompared to actual consumer feedback 1208 in order to adjust productattributes 1201).

[0171] Feedback, whether objective and/or subjective in nature,regarding performance with respect to a range of products may be used todefine individual consumer performance response profiles. When there arepotentially a variety of underlying mechanisms contributing to aconsumer's concerns, his or her performance response profile pattern mayhelp the recommendation engine align his or her target substrates withother consumers who have common underlying problem mechanisms. Differentunderlying problems may be addressed differently by various products.For example, acne has several causes (microbial, desquamatory,inflammatory, and the like) that typical consumers could not distinguishusing their senses alone, but which may be distinguished with the aid ofdiagnostic device measurements and/or by detecting the pattern ofperformance responses to different classes of products and actives.

[0172] Where the systems employ a neural network consumer feedback isused to enlarge and update the training set. The new feedback providesadditional training examples used to reconstruct the neural network. Forinstance, consumer feedback may be used to adjust connection weights ofthe algorithms in the invisible layers of the neural network. In someimplementation of a neural network, the updated training set is thenused to adjust product attribute ratings for each consumer segmentemployed in some embodiments of the invention. As the number of consumerresponses gathered by the invention increases, the accuracy andstability of the product attributes improves. In this re-training modeof operation, termed the “reverse intelligence engine” as shown in FIG.12, the neural network uses consumer responses and outputs of theinvention's forward intelligence or product recommendation engine asinputs and optimizes product attributes to improve recommendationaccuracy in an iterative process. Objectives of this re-training arenumerous and include improving the accuracy of future recommendations,generating insights on product performance for the purpose of productdevelopment, and the like. The invention may also improve the accuracyof predictions for each consumer as it learns more about the consumer'ssubjective and/or objective responses to products.

[0173] Part of the learning function referred to above may includeperiodically determining whether the feedback supports any bases forgrouping consumers in a way that narrows the standard deviation ofpreference and/or performance distributions within any clientneighborhood. If such bases are found they are incorporated into thealgorithms of the product recommendation engine and used to subsetappropriate neighborhoods when generating predicted performance and/orpreference ratings and the like. It is important to note that whileknowledge of a product's effect is made more accurate as the standarderror of the mean is reduced, predicting an individual's responsedepends on the width of the distribution of effects measured over thepopulation of similar users. It is assumed the distribution reflectsprimarily true variety of response and not measurement accuracy. Thestandard deviation needs to be as narrow as possible to increaseprediction accuracy. Therefore, reducing the limiting distance of acollaborative set involves a trade-off between reducing the standarddeviation of the prediction (to improve precision) and increasing thestandard error of the estimate (reducing accuracy) because the number ofconsumers contributing sample information is smaller.

[0174] Professional Integration

[0175] Professional integration refers to use of the invention byprofessional service providers. Typically professional integrationinvolves the construction of a professional interface, which maycomprise a series of software and/or specialized diagnostic tools thatallow professionals access to consumer data and characterizations(individual and/or populations). Professional integration also refers tomethods of referring a consumer served by the invention to aprofessional service provider (such as a physician in the case ofmedical targets) when conditions outside of the normal range of valuesare detected by the invention or when the system has objective orsubjective feedback data indicating a professional service rates highlyto address the concerns of a consumer. As data on professionalassessments of needs, causes thereof, prescribed treatments, and/orfeedback from professionally recommended products is acquired, theinvention's product recommendation engine may be re-trained and theinvention itself may aid professionals in their needs assessments,treatment recommendations, and the like. In particular, where medicaltargets are involved the data analysis performed by certain embodimentsof the invention may detect patterns of responses that may beinstrumental in predicting best therapeutic options to use to treatdisease conditions and the like. The converse may be true as well.

[0176] Embodiments of the invention may be implemented wherein productrecommendations (e.g., predicted performance, predicted preference, andthe like) and/or ancillary information for specific products andservices obtained from a professional (e.g., prescription drugs and thelike) are reported to the professionals but not directly to theconsumers. FIG. 13 illustrates in functional form how some professionalonly embodiments 1300 of the invention may operate. Block 1301represents a database wherein product and consumer informationcomprising at least a portion of the invention's knowledge base. Block1302 represents the data processing portion of a product recommendationengine that operates on data drawn from database 1301 and a request froman individual consumer and/or a professional servicing that consumer togenerate product recommendations 1303. Block 1304 represents aprofessional service provider who is the only person to receive theproduct recommendations 1303 generated by system 1300. Professional 1304conveys that information he or she deems appropriate to the individualconsumer/client 1306. Professional 1304 provides feedback about theindividual consumer/client 1306 to system 1300 him or herself via aprofessional interface 1305. Consumer/client 1306 provides objectivefeedback 1307 such as diagnostic data to system 1300 as well.

[0177] Another embodiment of the invention comprises an implementationwherein a consumer authorizes a professional service provider access tohis or her data. FIG. 14 illustrates in functional form how one suchembodiment 1400 of the invention operates. With permission from theconsumer/client 1304 his or her professional service provider 1402(e.g., a physician where a medical target is involved) accesses theinvention via professional interface 1404 to view consumer progress onhistorical, current, and/or proposed treatments, client characterizationdisplays, product recommendations (“recos”) for the consumer, and thelike 1402.

[0178] Yet another embodiment of the invention may be implementedwherein both consumers and professionals may access data within andrecommendations generated by the invention. In these embodiments, directconsumer access to the invention might be limited to viewing progressindicators (if provided), understanding their condition, and the like.Potential bases for implementing such an embodiment include, by way ofexample only, when diagnostic assessments of treatment performance canonly be performed by a professional, when most treatments requireprofessional administration, and the like. In the case of a medicaltarget for example, acne patients could be characterized by theinvention as to lesion type, distribution, stage, and patient conditionand history parameters. In this case the invention would predictperformance of alternative treatments based on continuous training ofthe predictive function. Input and access would be primarily availableto physicians.

[0179]FIG. 15 depicts in functional form how one such embodiment 1500 ofthe invention operates. Block 1501 represents a database wherein productand consumer information (gathered from both consumers and professionalservice providers) comprising at least a portion of the invention'sknowledge base is stored. Block 1502 represents the data processingportion of a product recommendation engine that operates on data drawnfrom database 1501 and a request from an individual consumer and/or aprofessional servicing that consumer/client to generate output 1503(e.g., product recommendations and/or ancillary information). Block 1504represents a professional service provider and block 1506 represents theconsumer/client for whom outputs 1503 were generated. The outputs 1503are available to both consumer/client 1506 and professional 1504.Alternatively, all outputs (“p+c” for professional and consumer/clientoutputs) are available to the professional 1504 while a more limitednumber of the outputs (“c” for client outputs) are available toconsumer/client 1506. Consumer/client 1506 provides feedback 1507 (e.g.,diagnostic data, preference data, and the like) to system 1300 viaprofessional interface 1505. Alternatively, professional 1304 providesfeedback (not shown) about the individual consumer/client 1506 to system1300 him or herself via the professional interface 1505 as well.

[0180] Where a professional is knowledgeable about an implementation ofthe invention, he or she may have a client begin using the invention asa consumer. Because professionals often have access to diagnosticcharacterizations (whether specialized or not), they often will be ableto obtain and input into the invention a solid baseline assessment ofthe new consumer (i.e., new client or new user). Thereafter, dependingon the implementation of the invention, the professional may be able tomonitor the progress of the client/consumer via the invention. Forinstance, where the invention includes imaging capabilities adermatology professional may enroll a patient and use the invention totrack patient progress over time by monitoring changes in stored images.The invention may also allow the professional to annotate images withcomments, indicate on the images important features or regions, and thelike. Using feature analysis and intelligent processing, someembodiments of the invention may automatically register and align imagescollected at different times and quantify changes. The source of theimages may be the professional, the consumer, other sources, or somecombination thereof. Thus, such an implementation of the invention canbe used in conjunction with visits by or to a professional as a way ofincreasing the frequency of monitoring. The professional could reviewclient/consumer data from the invention (e.g., substrate images) at aconvenient time and then contact the client/consumer to discuss the dataand/or request that the patient/consumer and professional schedule ameeting.

[0181] Certain embodiments of the invention may collect and store data(e.g., images, physical characterizations, and the like) onclients/consumers assessed by professionals as to underlying conditionsand/or causes. These embodiments may utilize any number of predeterminedcriteria for diagnostic accuracy (e.g., in the case of medical targetspercentage of missed diagnoses when disease is present, percentage ofwrong diagnosis when disease is diagnosed, and the like) to re-traininvention's product recommendation engine (e.g., collaborative filters,neural networks, and the like). In this way the invention may aid theprofessional in earlier assessment of needs, causes, conditions,treatments, and the like than otherwise would be possible. Conversely,in another embodiment the invention may detect a need/condition thatwarrants professional treatment and advise the consumer to seek thesame. For example, the invention could be programmed to monitor forpotentially adverse conditions known to be associated with a particularproduct, and where detected, advise the consumer to contact theappropriate professional. Where medical targets are involved theinvention also could compile and forward such information to anappropriate regulatory authority such as the Federal Food and DrugAdministration (FDA).

[0182] Various embodiments of the invention may allow consumers and/orprofessionals to access ancillary output such as textual content relatedto specific conditions, treatments, and the like within a relevantproduct category (e.g., skin care). Professional content, largelyscientific literature, may be segregated from nonprofessional content.Content searching tools may be provided as well.

[0183] Preferred embodiments of the invention involving certain forms ofprofessional integration have some means of identifying whether aproduct is being used as part of a professional service. In theseembodiments data on patients (versus consumers) will not contribute tothe nonprofessional understanding of the effectiveness ofnonprofessional products (e.g., non-prescription drugs in the case ofmedical targets). Two issues form the bases for this division of data, areporting issue and a differential placebo effect issue. The firstinvolves who provides the data input to the invention. Data for consumersystems is self-reported and self-rated. Data for professional systemsis evaluated and reported by a professional and therefore likely to bemore quantitative and more objective than consumer reported and rateddata. With regard to the second issue, product efficacy may be affectedby the attitudes of the user. Professional treatment may change thebehavior of a client in a way that materially effects target substratecondition. Differential placebo effects operate in almost every clinicalstudy environment. One can therefore expect similar phenomenon operatingin a professional environment, particularly where medical targets andproducts are involved.

[0184] Certain embodiments of the invention can assist in datacollection for clinical trials of new products. Typical clinical trialsinvolve a vehicle or placebo and an active product that are tested amongtwo populations of subjects. The subjects are randomly assigned to theactive product or the placebo. The invention ensures all subjects meetthe entrance requirements and any image data is graded blind to site ortime point. The efficacy results of the active product can be comparedto other available treatments and indications identified for consumersresponding best to the new treatment (i.e., active product). Theinvention can also be used to compare the results of clinical trialswith actual use of the new product by consumers outside the clinicalparadigm.

[0185] Another use of the invention relates to the training ofprofessionals. In the case of medical professionals such asdermatologists, the current method of training typically involvesviewing individual patients and is restricted to small, random samplesizes. The database portion of the invention will contain a large amountof real-world data regarding target substrates and their response tovarious treatments over time. For example, in certain embodiments of theinvention the database will have many images of normal and diseasedskin, as well as data related to aging, the effects of sun andenvironmental exposure, and the effects of various skin-care products.The database also would contain data on treatments and how those imageschange as a function of the treatments over time. This data could be ofgreat value in training professionals.

[0186] Methodologies

[0187]FIGS. 16 and 17 depict in functional and flow diagram formrespectively a typical interaction of a consumer with certainembodiments of the invention. The process depicted in FIG. 16 mirrorsfor the most part that depicted in FIG. 17. As shown in FIG. 17A,process 1700 starts at step 1701 and, in step 1705, the inventionreceives a request from a consumer for product recommendations. In step1710, the invention solicits input from the consumer, which is receivedat step 1715. As discussed above, input may comprise a wide variety ofinformation including personal profile information, concern areas,severity and importance for each concern area, preferences for productsused recently, and the like. In step 1720 the invention creates aconsumer profile in its consumer database or other storage, and thengenerates product recommendations with its recommendation engine. Instep 1725, the invention presents its recommendations to the consumer.Note also, the invention may present and/or the consumer may request andreceive ancillary information output at this point in the process aswell. In step 1730, the invention receives notification of theproduct(s) selected by the consumer for use. In step 1735, the inventionpresents the consumer with a recommended feedback interval. In step 1740(FIG. 17B), the invention waits for feedback to be received from theconsumer.

[0188] In step 1745, the invention receives a request from the consumerto provide feedback on the product(s) previously selected andsubsequently used. In step 1750, the invention receives feedback datafrom the consumer. As discussed above, feedback may comprise subjectiveand/or objective data regarding actual performance and preference forthe product(s) used by the consumer. In step 1755, the invention updatesthe consumer's profile, and in step 1760 the invention generates anddelivers to the consumer progress indicators. In step 1765, theinvention queries the consumer whether he or she will continue using theproduct(s). If yes, the process returns to step 1740 and waits for morefeedback from the consumer. If no, the process in step 1770 queries theconsumer whether he or she would like to select a new or differentproduct to use. If yes, the process returns to step 1720. If no, theprocess ends in step 1775.

[0189]FIG. 18 illustrates in flow diagram form a process 1800 forre-training the recommendation engine in accordance with certainembodiments of the invention. The process starts in step 1801 and, instep 1805, the invention receives individual input, creates individualprofiles, and generates individualized product recommendations for arelevant population of consumers. In step 1810, the invention isinformed of which products the individual consumers select for use. Instep 1815, the invention receives feedback from the individual consumersregarding their use of the previously selected products. In step 1820,the invention determines whether the feedback received in step 1815warrants re-training of the product recommendation engine. If yes, theinvention in step 1830 re-trains the product recommendation engine basedon the feedback received in step 1815 and then returns to wait step1825. If no, the invention in step 1825 waits for some predeterminedamount of time, some predetermined number of feedback interactions, amanual command, or the like before returning to step 1820.

[0190]FIG. 19 illustrates some of the concepts and potential revenuestreams that may be realized with various embodiments of the invention.The invention can be a component of a product distribution system.Operating as a service provided over the Internet (or through the mailor by phone order), the invention facilitates transactions based on theconsumer's educated selection process. The invention collects andcreates previously unavailable high quality product performance andpreference information, thereby creating novel revenue streams atmultiple points within systems incorporating the invention. FIG. 19identifies a number of these points with dollar signs (“$”). Also,whether incorporated within a wider a system or not, the knowledgeaccumulated and created by the invention has value to variousshareholder groups including, but not limited to, consumers andthird-party entities such as professional and non-professional serviceproviders (e.g., medical and non-medical professionals), distributionchain entities (e.g., retail stores, wholesalers, and the like), productdevelopers, marketing personnel, market analysts, and the like.Knowledge comprises any information gathered by, created by, containedor stored within the various elements of the invention.

[0191] Two revenue streams generated by the invention present themselvesin the form of product recommendation fees 1902 and consumersubscriptions 1901. In the case of product recommendation fees 1902 aconsumer pays a fee in exchange for receiving the productrecommendations. In the case of subscriptions 1901, consumers pay a feein exchange for ongoing access to invention recommendation servicesbecause of, among other things, the unique historical data the inventionstores regarding the individual history of each subscriber. The historymay include any number of items including, but not limited to, thephysical and subjective responses of the consumer and/or their targetsubstrate to particular products, weather, other relevant conditions,and the like. Over time, the invention obtains expertise in predictingeach consumer's future responses by better characterization of theconsumer-subscriber and more accurate alignment of theconsumer-subscriber with other relevant segments of the population.

[0192] In some embodiments, a consumer can pay additional fees forpremium services. For example, a consumer subscribing to the standardlevel of service may interact with the invention via a menu. A consumersubscribing to premium services may interact with the invention via amenu and/or a live person. Yet another level of service may involveinteracting via a simulated persona.

[0193] In other embodiments of the invention, a consumer may choose froma plurality of rate plans where each rate specifies a plurality offactors such as, interaction method (home, spa, computer, and the like),minutes of interaction time, storage space (images, history, and thelike), minutes of professional time, and the like. Standard level ofservice could provide top-N products by performance or preference. Ahigher subscription level would provide information on any number orproducts as well as arbitrarily named products. Another basis fordifferentiating subscription levels is on the diagnostic variablestracked for the subscriber. Basic level could involve no physicalparameters, higher levels could include a few parameters, and thehighest levels image based parameters. Service levels could be definedby the consumer's selection from a series of choices including but notlimited to frequency of access, number of products rated, number ofimages stored per year, particular parameters chosen for monitoring, andthe like.

[0194] Another revenue stream generated by the invention presents itselfin the form of kiosks and other remote site access 1903. Kiosks(providing access to recommendations, ancillary information output,category wide product information, and the like) provide a way forconsumers to access the invention at the site of product sales orwithout having personal Internet access. Ideal sites include whereverproducts are sold or near expert assistance. The services offered bykiosk may be at an introductory level and free to new consumers.Existing subscribers could access all information including those thatincur charges to their account. Pre-paid cards could be sold or creditcards accepted for services as well.

[0195] The kiosk-based system may ask for consumer login information, orfor new users, login information would be provided to permit easierfuture use. Log in could be biometric based. For new consumers a fewquestions might be presented to understand the general needs of theconsumer. The invention might provide top-N recommended productsfiltered by availability at that store or for all stores at the locationof the kiosk. The consumer also may be able to see recommended productsavailable through mail order (unfiltered). In either display the outputshould be standardized (e.g., performance and preference sores andprice.) For products not available at the location of the kiosk theconsumer may have the option of selecting mail order. Various revenueexchanges are possible with this service. Consumers also could receivecoupons to shop at the location. The operator of the invention mayreceive a commission when a kiosk-issued coupon is used locally. Kioskscould be placed in locations such as train stations, airports, malls,department stores, resorts, gyms, health spas, hair salons, any locationwhere consumers wait to receive services, and the like.

[0196] Because certain implementations of the invention may beinternational in scope, the knowledge accumulated by the inventionlikely includes information on brands and/or categories of products notcurrently available in all areas of the world. The knowledge accumulatedby the invention therefore can be used to facilitate identification ofnew brands, products, and/or ingredients that may prove successful innew markets. Information on the efficacy of products covered by animplementation of the invention can be made available for a fee and/orthe most effective products in various categories could be madeavailable in a static database of recommended products. Implementationsof the invention also could integrate distribution facilities and/orfunctions for both domestic and foreign products.

[0197] Another potential revenue stream that may be realized presentsitself in the form of data mining 1904. The invention's knowledge can bemined for intelligence of value to industrial components with interestin a particular product category. Data mined from the invention orintelligence may include, by way of example only, product performanceand/or preferences among any segment of a market, objective performanceand/or perceived performance for every category concern for any product,comparative performance between products, and the like. Additional datathat can be mined from invention databases that could be valuable toindustry includes, which consumers use and prefer which products, brandswitching and loyalty data, product interactions and regimen effects;trends in population demographics and needs, and the like. Mined data orintelligence could be sold to entities formulating new products,entities wishing to document, test and/or validate new claims forproducts, or entities seeking competitive evaluation of products.

[0198] Another revenue stream that may be realized involves brokeringservices 1904. Products used by consumers may be identified by theinvention that meet the criteria of companies interested in acquiringnew products or technology in the target category. The operator of theinvention can charge a fee for identifying products meeting the criteriaof an acquirer. This method of identifying suitable products likely isbetter than simply monitoring purchases of new products because datafrom the invention may be available sooner and allow for easierseparation of performance from preference and marketing factors.

[0199] Another revenue stream that may be realized involves payment of acommission for products purchased and/or consumers referred 1905. When aconsumer orders a product directly from the operator of the inventionfor example (in embodiments where this type of purchase is an option),the product may be supplied by mail from an allied distribution company.The allied distribution company pays a commission on the sale to theoperator of the invention in exchange. Coupon sales provide anotherexample. Where a consumer wishes to purchase a recommended productthrough conventional shopping outlets, a link between the recommendationand purchase of the product may be created by issuing a coupon orvoucher to the consumer. Manufacturers and/or retail outlets would offerthe coupon as an incentive to select a particular recommended productand/or purchase a particular recommended product from a certainretailer. The consumer receives the discount and the operator of theinvention receives a commission when the coupon accompanies the sale ofthe recommended product.

[0200] Revenue may be realized by directing a consumer to a section thatmay be incorporated in an embodiment of the invention devoted to newproducts. Manufacturers or other interested parties pay a fee to theoperator of the invention in exchange for placing the new product in thespecial section of the invention, for incorporating the new product intothe invention, and the like. Additional fees could be charged for accessto the data generated and/or accumulated by the invention that relatesto the new product.

[0201] Yet another revenue stream that may be realized with theinvention comprises a professional referral fee 1906. Where anembodiment of the invention capable of detecting serious abnormalconditions that warrant professional services does so, professionalservice providers can pay a fee to the operator of the invention to belisted as potential service provider and/or for actually receiving areferral from the invention.

[0202] Conclusion

[0203] While the invention has been described in connection with theembodiments depicted in the various figures, it is to be understood thatother embodiments may be used or modifications and additions may be madeto the described embodiments for performing the same function of theinvention without deviating from the spirit thereof. Therefore, theinvention should not be limited to any single embodiment, whetherexpressly depicted and described herein or not. Rather, the inventionshould be construed to have the full breadth and scope afforded by theclaims appended below.

We claim:
 1. A method of formulating individualized productrecommendations, comprising: receiving a first set of data from aconsumer regarding a target substrate; and generating a set ofindividualized product recommendations for the consumer from a pluralityof products within a product category, the generating comprising feedingthe first set of data as inputs into an intelligent performance-basedproduct recommendation engine, operating on the inputs with a dataprocessing portion of the product recommendation engine, and producing aset of outputs from the data processing portion of the productrecommendation engine, the outputs comprising the set of individualizedproduct recommendations.
 2. The method of claim 1 wherein the receivinga first set of data step comprises receiving a concern about thesubstrate.
 3. The method of claim 2 further comprising receiving aseverity of the concern.
 4. The method of claim 2 further comprisingreceiving an importance of the concern.
 5. The method of claim 1 furthercomprising receiving a second set of data from the consumer, the secondset of data comprising historical product data, and wherein the firstand second sets of data comprise the inputs into the productrecommendation engine.
 6. The method of claim 5 wherein the receiving asecond set of historical product data step comprises receivingperformance data for products used by the consumer in the past.
 7. Themethod of claim 5 wherein the receiving the second set of historicalproduct data step comprises receiving preference data for products usedby the consumer in the past.
 8. The method of claim 1 further comprisingreceiving a third set of data from the consumer, the third set of datacomprising personal profile information about the consumer, and whereinthe first and third sets of data comprise the inputs into the productrecommendation engine.
 9. The method of claim 1 wherein the operating onthe inputs with a data processing portion of the product recommendationengine step comprises operating on the inputs with a neural networkportion of the product recommendation engine.
 10. The method of claim 1wherein the operating on the inputs with a data processing portion ofthe product recommendation engine step comprises operating on the inputswith a collaborative filter portion of the product recommendationengine.
 11. The method of claim 1 wherein the operating on the inputswith a data processing portion of the product recommendation engine stepcomprises operating on the inputs with a content-based filter portion ofthe product recommendation engine.
 12. The method of claim 1 wherein theoperating on the inputs with a data processing portion of the productrecommendation engine comprises operating on the inputs with a cascadedcontent-based filter and collaborative filter portion of the productrecommendation engine.
 13. The method of claim 1 wherein the producing aset of outputs step comprises producing a first list of products and ascored predicted performance utility for each listed product.
 14. Themethod of claim 1 wherein the producing a set of outputs step comprisesproducing a first list of top-N products and a scored predictedperformance utility for each listed product.
 15. The method of claim 1wherein the producing a set of outputs step comprises producing a firstlist of products and a scored predicted preference utility for eachlisted product.
 16. The method of claim 1 wherein the producing a set ofoutputs step comprises producing a first list of top-N products and ascored predicted product preference utility for each listed product. 17.The method of claim 1 wherein the producing a first of outputs stepcomprises producing a first list of products and a purchase price foreach listed product.
 18. The method of claim 1 further comprisinggenerating ancillary information output from the product recommendationengine inputs.
 19. The method of claim 18 wherein the generatingancillary information output step comprises generating informationregarding effects of at least one of the products.
 20. The method ofclaim 18 wherein the generating ancillary information step comprisesgenerating information regarding the condition of the target substraterelative to a designated population of consumers.
 21. The method ofclaim 1 further comprising: communicating the set of individualizedproduct recommendations to the consumer.
 22. The method of claim 21wherein the communicating step comprises generating an and delivering aweb page containing the recommendations to the consumer.
 23. The methodof claim 1 further comprising: receiving feedback from the consumerregarding use of a product to treat the target substrate.
 24. The methodof claim 23 wherein the receiving feedback step comprises receivingfeedback from the consumer regarding use of a previously recommendedproduct.
 25. The method of claim 23 wherein the receiving feedback stepcomprises receiving preference data regarding the product.
 26. Themethod of claim 23 wherein the receiving feedback comprises receivingperformance data regarding the product.
 27. The method of claim 23further comprising: re-training the product recommendation engine basedon the feedback.
 28. The method of claim 1 wherein the receiving a firstset of data from a consumer step comprises receiving a first set of dataabout the consumer's skin, and the generating a set of individualizedproduct recommendations for the consumer step comprises generating a setof individualized product recommendations from a plurality of skin-careproducts.
 29. The method of claim 1 further comprising receiving apayment from the consumer.
 30. A method for improving productrecommendation quality, comprising: generating a plurality ofindividualized product recommendations for a given concern with anintelligent performance-based product recommendation engine; receivingfeedback from a plurality of consumers on use of products to treat theconcern; and re-training the product recommendation engine based on thefeedback received.
 31. The method of claim 30 wherein the receivingfeedback step comprises receiving product preference data.
 32. Themethod of claim 30 wherein the receiving feedback step comprisesreceiving product performance data.
 33. The method of claim 32 whereinthe receiving product performance data step further comprises receivingtarget substrate condition data.
 34. The method of claim 32 wherein thereceiving feedback step further comprises receiving subjective productperformance data.
 35. The method of claim 32 wherein the receivingfeedback step further comprises receiving objective product performancedata.
 36. The method of claim 35 wherein the receiving objective productperformance data step comprises receiving diagnostic data.
 37. Themethod of claim 30 wherein the re-training step comprises adjustingvalues of a plurality of product attributes in a neural network, theneural network comprising a portion of the product recommendationengine.
 38. The method of claim 30 wherein the re-training stepcomprises adjusting values of a plurality of connection weights in aneural network, the neural network comprising a portion of the productrecommendation engine.
 39. The method of claim 30 wherein there-training step comprises identifying relevant consumer segments basedon the feedback and grouping the consumers into the segments.
 40. Themethod of claim 30 wherein the re-training step comprises revising a setof collaborative neighborhood configurations in a collaborative filter,the collaborative filter comprising a portion of the productrecommendation engine.
 41. The method of claim 30 wherein there-training step comprises adding a personal profile information filterto a set of collaborative neighborhood configurations in collaborativefilter, the collaborative filter comprising a portion of the productrecommendation engine.
 42. A system for generating individualizedproduct recommendations, comprising: a database containing productinformation and consumer information; and an intelligentperformance-based product recommendation engine in communication withthe database, wherein the product recommendations are generated for aconsumer by the product recommendation engine in response to a requestreceived from the consumer, the product recommendation engine drawing ondata contained in the request and the information in the database togenerate the product recommendations.
 43. The system of claim 42 whereinthe product recommendation engine comprises a content-based filter. 44.The system of claim 42 wherein the product recommendation enginecomprises a neural network.
 45. The system of claim 44 wherein theneural network contains a plurality of attributes for each of aplurality of products within a plurality of product categories.
 46. Thesystem of claim 44 wherein the neural network receives as inputs aplurality of consumer characterization variables for the consumer andproduct performance data for a population of consumers.
 47. The systemof claim 44 wherein the neural network receives as inputs a plurality ofconsumer characterization variables for the consumer and productpreference data for a population of consumers.
 48. The system of claim42 wherein the product recommendation engine comprises a collaborativefilter, the collaborative filter defining a set of other consumerssimilar to the consumer associated with the request.
 49. The system ofclaim 42 wherein the product recommendation engine comprises a cascadedcollaborative and content-based filter.
 50. The system of claim 42further comprising a consumer interface communicably connected to theproduct recommendation engine.
 51. The system of claim 50 wherein thecommunicable connection comprises a computer network.
 52. The system ofclaim 50 wherein the communicable connection comprises atelecommunications network.
 53. The system of claim 50 wherein thecommunicable connection comprises the Internet.
 54. The system of claim50 wherein the consumer interface comprises a personal computer.
 55. Thesystem of claim 50 wherein the consumer interface comprises a diagnosticdevice.
 56. The system of claim 50 wherein the consumer interfacecomprises a camera.
 57. The system of claim 50 wherein the consumerinterface comprises a kiosk.
 58. The system of claim 57 wherein theproduct recommendations are limited to products available for purchasein the vicinity of the kiosk.
 59. The system of claim 50 wherein theconsumer interface is located at a professional service provider'soffice.
 60. The system of claim 50 wherein the consumer interface islocated at the consumer's home.
 61. The system of claim 42 wherein therequest comprises an identification of a target substrate and aparticular consumer, the consumer information stored in the databasecomprising a characterizations record for the particular consumer. 62.The system of claim 42 wherein the request is generated by the consumer.63. The system of claim 42 wherein the request is generated by aprofessional service provider.
 64. The system of claim 62 wherein therecord further comprises an importance of the concern.
 65. The system ofclaim 62 wherein the record further comprises a severity of the concern.66. The system of claim 62 wherein the record further comprises productpreference information.
 67. The system of claim 62 wherein the recordfurther comprises historical product preference information.
 68. Thesystem of claim 62 wherein the record further comprises historicalproduct performance information.
 69. The system of claim 62 wherein therecord further comprises personal profile information.
 70. The system ofclaim 42 wherein the product recommendations comprise a first list ofproducts and a scored predicted performance utility for each listedproduct.
 71. The system of claim 42 wherein the product recommendationscomprise a first list of top-N products and a scored predictedperformance utility for each listed product.
 72. The system of claim 42wherein the product recommendations comprise a first list of productsand a scored predicted preference utility for each listed product. 73.The system of claim 42 wherein the product recommendations comprise afirst list of top-N products and a scored predicted product preferenceutility for each listed product.
 74. The system of claim 42 wherein theproduct recommendations comprise a first list of products and a purchaseprice for each listed product.
 75. The system of claim 42 furthercomprising generating ancillary information output with the productrecommendation engine in response to the request.
 76. The system ofclaim 42 wherein a plurality of consumers provide ongoing feedbackregarding the use of products, portions of the feedback being stored inthe product information and consumer information databases.
 77. Thesystem of claim 76 wherein the feedback comprises product performancedata.
 78. The system of claim 76 wherein the feedback comprises productpreference data.
 79. The system of claim 76 wherein the feedbackcomprises subjective feedback.
 80. The system of claim 76 wherein thefeedback comprises objective feedback.
 81. The system of claim 76wherein the product recommendation engine is periodically re-trainedbased on the feedback.
 82. The system of claim 81 wherein there-training improves the quality of the product recommendations.
 83. Thesystem of claim 81 wherein the product recommendation engine comprises acollaborative filter having a plurality of collaborative neighborhoodsand the re-training comprises revising the collaborative neighborhoodsbased on performance response patterns of the consumers.
 84. The systemof claim 42 further comprising a professional interface communicablyconnected to the product recommendation engine.
 85. The system of claim84 wherein the communicable connection comprises the Internet.
 86. Thesystem of claim 84 wherein the professional interface comprises apersonal computer.
 87. The system of claim 84 wherein the professionalinterface comprises a diagnostic device.
 88. The system of claim 87wherein the device comprises a camera.
 89. A method for generatingproduct recommendations, comprising: operating an intelligentperformance-based product recommendation system, the system gatheringinformation from consumers, generating product recommendations for theconsumers, and analyzing the information and product recommendations toobtain knowledge.
 90. The method of claim 89 further comprisingdelivering the product recommendations to the consumers in exchange fora payment.
 91. The method of claim 90 wherein the delivering stepcomprises delivering the product recommendations to the consumers inexchange for a subscription payment.
 92. The method of claim 89 furthercomprising delivering the knowledge to a third-party in exchange for apayment.
 93. The method of claim 92 wherein the delivering stepcomprises delivering the knowledge to a professional service provider.94. The method of claim 92 wherein the delivering step comprisesdelivering the knowledge to a medical professional.
 95. The method ofclaim 92 wherein the delivering step comprises delivering the knowledgeto a product distribution chain entity.
 96. The method of claim 92wherein the delivering step comprises delivering the knowledge to aproduct developer.
 97. The method of claim 92 wherein the deliveringstep comprises delivering the knowledge to a product marketer.
 98. Themethod of claim 89 further comprising mining the knowledge to obtainintelligence and delivering the intelligence to a third-party inexchange for a fee.
 99. The method of claim 89 further comprisingidentifying a product meeting predefined criteria of an acquiring entitybased on the knowledge and notifying the acquiring entity about theidentified product in exchange for a payment.
 100. The method of claim89 further comprising receiving a payment from a selling entity wheneverthe consumers purchase a product within the product recommendations fromthe selling entity.
 101. The method of claim 89 wherein the gatheringinformation step further comprises directing the consumers to a newproduct portion of the system in exchange for a fee.
 102. The method ofclaim 89 further comprising identifying conditions of the consumerswarranting professional service provider treatment and referring theconsumers with the identified conditions to a professional serviceprovider in exchange for a fee.